Multi-factorial real-time predictive model for healthcare events

ABSTRACT

At least one aspect of this technical solution is directed to a system for generating a multi-factorial real-time predictive model for healthcare events, with a data processing system comprising memory and one or more processors to obtain a condition metric and a condition period associated with a participant object, generate, based on a model trained using historical condition-service data, a first participation metric for the participant object based on a participant service associated with the condition metric and the participant object, aggregate the first participation metric and the condition period into a first aggregated participation metric, receive, via a computing device associated with the participant object, a selection of an opportunity object associated with the condition metric, the opportunity object corresponding to an opportunity event provided via a third-party application that interfaces with the data processing system, generate, based on the model and the selection of the opportunity object, a second participation metric associated with the condition metric and the opportunity object, aggregate the second participation metric and the condition period into a second aggregated participation metric for the participant object, determine, based on the second participation metric being less than the first participation metric, a reduction in resource utilization, and provide, responsive to the determination of the reduction in resource utilization, an alert to the computing device indicating the reduction in resource utilization corresponding to the selection of the opportunity object.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 120 as a continuation of U.S. patent application Ser. No. 17/214,445, filed Mar. 26, 2021, which claims the benefit of priority under 35 U.S.C. § 119 to U.S. Provisional Patent Application Ser. No. 63/056,336, filed Jul. 24, 2020, all of which are hereby incorporated by reference herein in their entirety.

TECHNICAL FIELD

This disclosure relates generally to digital healthcare infrastructure, and more particularly to a multi-factorial real-time predictive model for healthcare events.

BACKGROUND

Individuals can obtain goods or services related to healthcare. Computing systems can provide information about such goods or services related to healthcare. However, as the number and types of goods and services increase, it can be challenging for computing systems to efficiently and reliably identify particular goods and services for an individual.

SUMMARY

Health care opportunities are increasing in complexity and scope in response to the expansion of health care services available to health care consumers. Participants in various health care support programs and health care accounts face increasingly complex and interwoven opportunities with time-sensitive conditions and complex interdependencies. However, conventional systems may not effectively model healthcare events associated with various services and accounts effectively to a participant based on real-time conditions associated with the participant. Participant in conventional systems may thus result in a loss of opportunities due to lack of computational technological systems to rapidly and securely communicate with heterogeneous systems centered on one or more of a participant's real-time associations and projected future interactions with services coupled to those systems. Thus, a technological solution of a multi-factorial real-time predictive model for healthcare events is desired.

At least one aspect of this technical solution is directed to a system for generating a multi-factorial real-time predictive model for healthcare events, with a data processing system comprising memory and one or more processors to obtain a condition metric and a condition period associated with a participant object, generate, based on a model trained using historical condition-service data, a first participation metric for the participant object based on a participant service associated with the condition metric and the participant object, aggregate the first participation metric and the condition period into a first aggregated participation metric, receive, via a computing device associated with the participant object, a selection of an opportunity object associated with the condition metric, the opportunity object corresponding to an opportunity event provided via a third-party application that interfaces with the data processing system, generate, based on the model and the selection of the opportunity object, a second participation metric associated with the condition metric and the opportunity object, aggregate the second participation metric and the condition period into a second aggregated participation metric for the participant object, determine, based on the second participation metric being less than the first participation metric, a reduction in resource utilization, and provide, responsive to the determination of the reduction in resource utilization, an alert to the computing device indicating the reduction in resource utilization corresponding to the selection of the opportunity object.

At least one aspect of this technical solution is directed to a system where the condition metric includes a health state, the first participation metric includes a first monetary amount associated with the health state, and the second participation metric includes a second monetary amount associated with the health state.

At least one aspect of this technical solution is directed to a system where the health state comprises a chronic health state including a plurality of instances, the first participation metric comprises a first monetary amount associated with a single instance of the chronic health state, and the second participation metric comprises a second monetary amount associated with a single instance of the chronic health state.

At least one aspect of this technical solution is directed to a system where the health state comprises a chronic health state associated with the condition period, the first participation metric includes a first monetary amount associated with the chronic health state and the condition period, and the second participation metric includes a second monetary amount associated with the chronic health state and the condition period.

At least one aspect of this technical solution is directed to a system where the reduction in resource utilization includes a predetermined difference between a numeric value of the first participation metric and the second participation metric.

At least one aspect of this technical solution is directed to a system where the data processing system is further operable to generate the historical condition-service data based on a first participant system operatively coupled to the data processing system and associated with the participant service.

At least one aspect of this technical solution is directed to a system where the data processing system is further operable to generate the historical condition-service data based on a second participant system operatively coupled to the data processing system and associated with a second participant service.

At least one aspect of this technical solution is directed to a system of claim 1, wherein the first participation metric includes a first service rating associated with the condition metric, and the second participation metric includes a second service rating associated with the condition metric.

At least one aspect of this technical solution is directed to a system where the first participation metric includes a first participant location, and the second participation metric includes a second participant location.

At least one aspect of this technical solution is directed to a system where the reduction in resource utilization includes a distance heuristic including the first participant location and the second participant location.

At least one aspect of this technical solution is directed to a method of generating a multi-factorial real-time predictive model for healthcare events, by obtaining a condition metric and a condition period associated with a participant object, generating, based on a model trained using historical condition-service data, a first participation metric for the participant object based on a participant service associated with the condition metric and the participant object, aggregating the first participation metric and the condition period into a first aggregated participation metric, receiving a selection of an opportunity object associated with the condition metric, the opportunity object corresponding to an opportunity event provided via a third-party application that interfaces with the data processing system, generate, based on the model and the selection of the opportunity object, a second participation metric associated with the condition metric and the opportunity object, aggregating the second participation metric and the condition period into a second aggregated participation metric for the participation object, determining, based on the second participation metric being less than the first participation metric, a reduction in resource utilization; and providing, responsive to the determination of the reduction in resource utilization, an alert, to a computing device associated with the participant object, indicating the reduction in resource utilization corresponding to the selection of the opportunity object.

At least one aspect of this technical solution is directed to a method where the condition metric includes a health state, the first participation metric includes a first monetary amount associated with the health state, and the second participation metric includes a second monetary amount associated with the health state.

At least one aspect of this technical solution is directed to a method where the health state includes a chronic health state including a plurality of instances, the first participation metric includes a first monetary amount associated with a single instance of the chronic health state, and the second participation metric includes a second monetary amount associated with a single instance of the chronic health state.

At least one aspect of this technical solution is directed to a method where the health state includes a chronic health state associated with the condition period, the first participation metric includes a first monetary amount associated with the chronic health state and the condition period, and the second participation metric includes a second monetary amount associated with the chronic health state and the condition period.

At least one aspect of this technical solution is directed to a method where the reduction in resource utilization includes a predetermined difference between a numeric value of the first participation metric and the second participation metric.

At least one aspect of this technical solution is directed to a method further including generating the historical condition-service data based on a first participant system operatively coupled to the data processing system and associated with the participant service.

At least one aspect of this technical solution is directed to a method where the data processing system is further operable to generate the historical condition-service data based on a second participant system operatively coupled to the data processing system and associated with a second participant service.

At least one aspect of this technical solution is directed to a method where the first participation metric includes a first service rating associated with the condition metric, and the second participation metric includes a second service rating associated with the condition metric.

At least one aspect of this technical solution is directed to a computer readable medium including one or more instructions stored thereon and executable by a processor to, obtain a condition metric and a condition period associated with a participant object, generate, based on a model trained using historical condition-service data, a first participation metric for the participant object based on a participant service associated with the condition metric and the participant object, aggregate the first participation metric and the condition period into a first aggregated participation metric, receive, via a computing device associated with the participant object, a selection of an opportunity object associated with the condition metric, the opportunity object corresponding to an opportunity event provided by a third-party application that interfaces with the data processing system, generate, based on the model and the selection of the opportunity object, a second participation metric associated with the condition metric and the opportunity object, aggregate the second participation metric and the condition period into a second aggregated participation metric for the participant object, and provide, in response to the determination of the reduction in resource utilization, an alert to the computing device indicating the reduction in resource utilization corresponding to the selection of the opportunity object.

At least one aspect of this technical solution is directed to a computer readable medium where the computer readable medium further includes one or more instructions executable by a processor to generate the historical condition-service data based on a first participant system operatively coupled to the data processing system and associated with the participant service, and generate the historical condition-service data based on a second participant system operatively coupled to the data processing system and associated with a second participant service.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects and features of the present implementations will become apparent to those ordinarily skilled in the art upon review of the following description of specific implementations in conjunction with the accompanying figures, wherein:

FIG. 1A illustrates an example network environment including a client device in communication with a server device, in accordance with present implementations.

FIG. 1B illustrates an example cloud computing environment including a client device in communication with cloud service providers, in accordance with present implementations.

FIGS. 1C and 1D illustrate example computing devices in accordance with present implementations.

FIG. 2 illustrates an example cloud computing environment including an example data processing system, in accordance with present implementations.

FIG. 3 illustrates an example data processing system, in accordance with present implementations.

FIG. 4 illustrates an example participant database system further to the example data processing system of FIG. 3, in accordance with present implementations.

FIG. 5 illustrates an example opportunity database system further to the example data processing system of FIG. 3, in accordance with present implementations.

FIG. 6 illustrates an example predictive service engine, in accordance with present implementations.

FIG. 7 illustrates an example electronic device associated with an example data processing system and operating in an example temporal comparison modeling state, in accordance with present implementations.

FIG. 8 illustrates an example electronic device associated with an example data processing system and operating in an example geospatial comparison modeling state, in accordance with present implementations.

FIG. 9 illustrates an example method of generating a real-time trigger-based digital feed, in accordance with present implementations.

FIG. 10 illustrates an example method of generating a real-time trigger-based digital feed further to the method of FIG. 9, in accordance with present implementations.

DETAILED DESCRIPTION

The present implementations will now be described in detail with reference to the drawings, which are provided as illustrative examples of the implementations so as to enable those skilled in the art to practice the implementations and alternatives apparent to those skilled in the art. Notably, the figures and examples below are not meant to limit the scope of the present implementations to a single implementation, but other implementations are possible by way of interchange of some or all of the described or illustrated elements. Moreover, where certain elements of the present implementations can be partially or fully implemented using known components, only those portions of such known components that are necessary for an understanding of the present implementations will be described, and detailed descriptions of other portions of such known components will be omitted so as not to obscure the present implementations. Implementations described as being implemented in software should not be limited thereto, but can include implementations implemented in hardware, or combinations of software and hardware, and vice-versa, as will be apparent to those skilled in the art, unless otherwise specified herein. In the present specification, an implementation showing a singular component should not be considered limiting; rather, the present disclosure is intended to encompass other implementations including a plurality of the same component, and vice-versa, unless explicitly stated otherwise herein. Moreover, applicants do not intend for any term in the specification or claims to be ascribed an uncommon or special meaning unless explicitly set forth as such. Further, the present implementations encompass present and future known equivalents to the known components referred to herein by way of illustration.

A data processing system can provide insights for costs of healthcare-related goods and services using predictive analytics. The data processing system can identify and quantify relationships between a condition of an individual participant and cost to that individual participant. A condition can include a medical condition, health condition, health state, or chronic medical condition, for example. A cost can include a cost over a particular period of time, cost over a course of treatment, or lifetime spend associated with an individual participant. The data processing system can be in communication with at least one third party system associated with at least one specific medical benefit plan of an individual participant. As one example, the data processing system can determine a cost based on in-network providers, providers in a participant's geographic area, a quality rating for procedures in the participant's geographic area, and average cost insights for the participant for procedures in a specific geographic area corresponding to the participant, for example. The data processing system can thus generate quantitative insights for cost and care customized and specific to a particular participant. The data processing system can update cost in real-time based on real-time transactions. The data processing system can also use data associated with claim history, spend history, insurance provider, geolocation, and card transactions, for example, to generate quantitative insights for cost and care.

For purposes of reading the description of the various implementations below, the following descriptions of the sections of the specification and their respective contents can be helpful:

Section A describes a network environment and computing environment which can be useful for practicing implementations described herein.

Section B describes implementations of systems and methods for a multi-factorial real-time predictive model for healthcare events.

A. Computing and Network Environment

Prior to discussing specific implementations of the present solution, it can be helpful to describe aspects of the operating environment as well as associated system components (e.g., hardware elements) in connection with the methods and systems described herein. Referring to FIG. 1A, an implementation of a network environment is depicted. In brief overview, the network environment includes one or more clients 102 a-102 n (also generally referred to as local machine(s) 102, client(s) 102, client node(s) 102, client machine(s) 102, client computer(s) 102, client device(s) 102, endpoint(s) 102, or endpoint node(s) 102) in communication with one or more servers 106 a-106 n (also generally referred to as server(s) 106, node 106, or remote machine(s) 106) via one or more networks 104. In some implementations, a client 102 has the capacity to function as both a client node seeking obtain to resources provided by a server and as a server providing obtain to hosted resources for other clients 102 a-102 n.

Although FIG. 1A shows a network 104 between the clients 102 and the servers 106, the clients 102 and the servers 106 can be on the same network 104. In some implementations, there are multiple networks 104 between the clients 102 and the servers 106. In one of these implementations, a network 104′ (not shown) can be a private network and a network 104 can be a public network. In another of these implementations, a network 104 can be a private network and a network 104′ a public network. In still another of these implementations, networks 104 and 104′ can both be private networks.

The network 104 can be connected via wired or wireless links. Wired links can include Digital Subscriber Line (DSL), coaxial cable lines, or optical fiber lines. The wireless links can include BLUETOOTH, Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), an infrared channel or satellite band. The wireless links can also include any cellular network standards used to communicate among mobile devices, including standards that qualify as 1G, 2G, 3G, or 4G. The network standards can qualify as one or more generation of mobile telecommunication standards by fulfilling a specification or standards such as the specifications maintained by International Telecommunication Union. The 3G standards, for example, can correspond to the International Mobile Telecommunications-2000 (IMT-2000) specification, and the 4G standards can correspond to the International Mobile Telecommunications Advanced (IMT-Advanced) specification. Examples of cellular network standards include AMPS, GSM, GPRS, UMTS, LTE, LTE Advanced, Mobile WiMAX, and WiMAX-Advanced. Cellular network standards can use various channel access methods e.g. FDMA, TDMA, CDMA, or SDMA. In some implementations, different types of data can be transmitted via different links and standards. In other implementations, the same types of data can be transmitted via different links and standards.

The network 104 can be any type and/or form of network. The geographical scope of the network 104 can vary widely and the network 104 can be a body area network (BAN), a personal area network (PAN), a local-area network (LAN), e.g. Intranet, a metropolitan area network (MAN), a wide area network (WAN), or the Internet. The topology of the network 104 can be of any form and can include, e.g., any of the following: point-to-point, bus, star, ring, mesh, or tree. The network 104 can be an overlay network which is virtual and sits on top of one or more layers of other networks 104′. The network 104 can be of any such network topology as known to those ordinarily skilled in the art capable of supporting the operations described herein. The network 104 can utilize different techniques and layers or stacks of protocols, including, e.g., the Ethernet protocol, the internet protocol suite (TCP/IP), the ATM (Asynchronous Transfer Mode) technique, the SONET (Synchronous Optical Networking) protocol, or the SDH (Synchronous Digital Hierarchy) protocol. The TCP/IP internet protocol suite can include application layer, transport layer, internet layer (including, e.g., IPv6), or the link layer. The network 104 can be a type of a broadcast network, a telecommunications network, a data communication network, or a computer network.

In some implementations, the system can include multiple, logically-grouped servers 106. In one of these implementations, the logical group of servers can be referred to as a server farm 38 or a machine farm 38. In another of these implementations, the servers 106 can be geographically dispersed. In other implementations, a machine farm 38 can be administered as a single entity. In still other implementations, the machine farm 38 includes a plurality of machine farms 38. The servers 106 within each machine farm 38 can be heterogeneous—one or more of the servers 106 or machines 106 can operate according to one type of operating system platform (e.g., WINDOWS NT, manufactured by Microsoft Corp. of Redmond, Wash.), while one or more of the other servers 106 can operate on according to another type of operating system platform (e.g., Unix, Linux, or Mac OS X).

In one implementation, servers 106 in the machine farm 38 can be stored in high-density rack systems, along with associated storage systems, and located in an enterprise data center. In this implementation, consolidating the servers 106 in this way can improve system manageability, data security, the physical security of the system, and system performance by locating servers 106 and high performance storage systems on localized high performance networks. Centralizing the servers 106 and storage systems and coupling them with advanced system management tools allows more efficient use of server resources.

The servers 106 of each machine farm 38 do not need to be physically proximate to another server 106 in the same machine farm 38. Thus, the group of servers 106 logically grouped as a machine farm 38 can be interconnected using a wide-area network (WAN) connection or a metropolitan-area network (MAN) connection. For example, a machine farm 38 can include servers 106 physically located in different continents or different regions of a continent, country, state, city, campus, or room. Data transmission speeds between servers 106 in the machine farm 38 can be increased if the servers 106 are connected using a local-area network (LAN) connection or some form of direct connection. Additionally, a heterogeneous machine farm 38 can include one or more servers 106 operating according to a type of operating system, while one or more other servers 106 execute one or more types of hypervisors rather than operating systems. In these implementations, hypervisors can be used to emulate virtual hardware, partition physical hardware, virtualize physical hardware, and execute virtual machines that provide access to computing environments, allowing multiple operating systems to run concurrently on a host computer. Native hypervisors can run directly on the host computer. Hypervisors can include VMware ESX/ESXi, manufactured by VMWare, Inc., of Palo Alto, Calif.; the Xen hypervisor, an open source product whose development is overseen by Citrix Systems, Inc.; the HYPER-V hypervisors provided by Microsoft or others. Hosted hypervisors can run within an operating system on a second software level. Examples of hosted hypervisors can include VMware Workstation and VIRTUALBOX.

Management of the machine farm 38 can be de-centralized. For example, one or more servers 106 can comprise components, subsystems and modules to support one or more management services for the machine farm 38. In one of these implementations, one or more servers 106 provide functionality for management of dynamic data, including techniques for handling failover, data replication, and increasing the robustness of the machine farm 38. Each server 106 can communicate with a persistent store and, in some implementations, with a dynamic store.

Server 106 can be a file server, application server, web server, proxy server, appliance, network appliance, gateway, gateway server, virtualization server, deployment server, SSL VPN server, or firewall. In one implementation, the server 106 can be referred to as a remote machine or a node. In another implementation, a plurality of nodes 290 can be in the path between any two communicating servers.

Referring to FIG. 1B, a cloud computing environment is depicted. A cloud computing environment can provide client 102 with one or more resources provided by a network environment. The cloud computing environment can include one or more clients 102 a-102 n, in communication with the cloud 108 over one or more networks 104. Clients 102 can include, e.g., thick clients, thin clients, and zero clients. A thick client can provide at least some functionality even when disconnected from the cloud 108 or servers 106. A thin client or a zero client can depend on the connection to the cloud 108 or server 106 to provide functionality. A zero client can depend on the cloud 108 or other networks 104 or servers 106 to retrieve operating system data for the client device. The cloud 108 can include back end platforms, e.g., servers 106, storage, server farms or data centers.

The cloud 108 can be public, private, or hybrid. Public clouds can include public servers 106 that are maintained by third parties to the clients 102 or the owners of the clients. The servers 106 can be located off-site in remote geographical locations as disclosed above or otherwise. Public clouds can be connected to the servers 106 over a public network. Private clouds can include private servers 106 that are physically maintained by clients 102 or owners of clients. Private clouds can be connected to the servers 106 over a private network 104. Hybrid clouds 108 can include both the private and public networks 104 and servers 106.

The cloud 108 can also include a cloud based delivery, e.g. Software as a Service (SaaS) 110, Platform as a Service (PaaS) 112, and Infrastructure as a Service (IaaS) 114. IaaS can refer to a user renting the use of infrastructure resources that are needed during a specified time period. IaaS providers can offer storage, networking, servers or virtualization resources from large pools, allowing the users to quickly scale up by accessing more resources as needed. Examples of IaaS can include infrastructure and services (e.g., EG-32) provided by OVH HOSTING of Montreal, Quebec, Canada, AMAZON WEB SERVICES provided by Amazon.com, Inc., of Seattle, Wash., RACKSPACE CLOUD provided by Rackspace US, Inc., of San Antonio, Tex., Google Compute Engine provided by Google Inc. of Mountain View, Calif., or RIGHTSCALE provided by RightScale, Inc., of Santa Barbara, Calif. PaaS providers can offer functionality provided by IaaS, including, e.g., storage, networking, servers or virtualization, as well as additional resources such as, e.g., the operating system, middleware, or runtime resources. Examples of PaaS include WINDOWS AZURE provided by Microsoft Corporation of Redmond, Wash., Google App Engine provided by Google Inc., and HEROKU provided by Heroku, Inc. of San Francisco, Calif. SaaS providers can offer the resources that PaaS provides, including storage, networking, servers, virtualization, operating system, middleware, or runtime resources. In some implementations, SaaS providers can offer additional resources including, e.g., data and application resources. Examples of SaaS include GOOGLE APPS provided by Google Inc., SALESFORCE provided by Salesforce.com Inc. of San Francisco, Calif., or OFFICE 365 provided by Microsoft Corporation. Examples of SaaS can also include data storage providers, e.g. DROPBOX provided by Dropbox, Inc. of San Francisco, Calif., Microsoft SKYDRIVE provided by Microsoft Corporation, Google Drive provided by Google Inc., or Apple ICLOUD provided by Apple Inc. of Cupertino, Calif.

Clients 102 can access IaaS resources with one or more IaaS standards, including, e.g., Amazon Elastic Compute Cloud (EC2), Open Cloud Computing Interface (OCCI), Cloud Infrastructure Management Interface (CIMI), or OpenStack standards. Some IaaS standards can allow clients access to resources over HTTP, and can use Representational State Transfer (REST) protocol or Simple Object Access Protocol (SOAP). Clients 102 can access PaaS resources with different PaaS interfaces. Some PaaS interfaces use HTTP packages, standard Java APIs, JavaMail API, Java Data Objects (JDO), Java Persistence API (JPA), Python APIs, web integration APIs for different programming languages including, e.g., Rack for Ruby, WSGI for Python, or PSGI for Perl, or other APIs that can be built on REST, HTTP, XML, or other protocols. Clients 102 can access SaaS resources through the use of web-based user interfaces, provided by a web browser (e.g. GOOGLE CHROME, Microsoft INTERNET EXPLORER, or Mozilla Firefox provided by Mozilla Foundation of Mountain View, Calif.). Clients 102 can also access SaaS resources through smartphone or tablet applications, including, e.g., Salesforce Sales Cloud, or Google Drive app. Clients 102 can also access SaaS resources through the client operating system, including, e.g., Windows file system for DROPBOX.

In some implementations, access to IaaS, PaaS, or SaaS resources can be authenticated. For example, a server or authentication server can authenticate a user via security certificates, HTTPS, or API keys. API keys can include various encryption standards such as, e.g., Advanced Encryption Standard (AES). Data resources can be sent over Transport Layer Security (TLS) or Secure Sockets Layer (SSL).

The client 102 and server 106 can be deployed as and/or executed on any type and form of computing device, e.g. a computer, network device or appliance capable of communicating on any type and form of network and performing the operations described herein. FIGS. 1C and 1D depict block diagrams of a computing device 100 useful for practicing an implementation of the client 102 or a server 106. As shown in FIGS. 1C and 1D, each computing device 100 includes a central processing unit 121, and a main memory unit 122. As shown in FIG. 1C, a computing device 100 can include a storage device 128, an installation device 116, a network interface 118, an I/O controller 123, display devices 124 a-124 n, a keyboard 126 and a pointing device 127, e.g. a mouse. The storage device 128 can include, without limitation, an operating system, software, and a software of an data processing system (DPS) 120. As shown in FIG. 1D, each computing device 100 can also include additional optional elements, e.g. a memory port 103, a bridge 170, one or more input/output devices 130 a-130 n (generally referred to using reference numeral 130), and a cache memory 140 in communication with the central processing unit 121.

The central processing unit 121 is any logic circuitry that responds to and processes instructions fetched from the main memory unit 122. In many implementations, the central processing unit 121 is provided by a microprocessor unit, e.g.: those manufactured by Intel Corporation of Mountain View, Calif.; those manufactured by Motorola Corporation of Schaumburg, Ill.; the ARM processor and TEGRA system on a chip (SoC) manufactured by Nvidia of Santa Clara, Calif.; the POWER7 processor, those manufactured by International Business Machines of White Plains, N.Y.; or those manufactured by Advanced Micro Devices of Sunnyvale, Calif. The computing device 100 can be based on any of these processors, or any other processor capable of operating as described herein. The central processing unit 121 can utilize instruction level parallelism, thread level parallelism, different levels of cache, and multi-core processors. A multi-core processor can include two or more processing units on a single computing component. Examples of multi-core processors include the AMD PHENOM IIX2, INTEL CORE i5 and INTEL CORE i7.

Main memory unit 122 can include one or more memory chips capable of storing data and allowing any storage location to be directly accessed by the microprocessor 121. Main memory unit 122 can be volatile and faster than storage 128 memory. Main memory units 122 can be Dynamic random access memory (DRAM) or any variants, including static random access memory (SRAM), Burst SRAM or SynchBurst SRAM (B SRAM), Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended Data Output RAM (EDO RAM), Extended Data Output DRAM (EDO DRAM), Burst Extended Data Output DRAM (BEDO DRAM), Single Data Rate Synchronous DRAM (SDR SDRAM), Double Data Rate SDRAM (DDR SDRAM), Direct Rambus DRAM (DRDRAM), or Extreme Data Rate DRAM (XDR DRAM). In some implementations, the main memory 122 or the storage 128 can be non-volatile; e.g., non-volatile read access memory (NVRAM), flash memory non-volatile static RAM (nvSRAM), Ferroelectric RAM (FeRAM), Magnetoresistive RAM (MRAM), Phase-change memory (PRAM), conductive-bridging RAM (CBRAM), Silicon-Oxide-Nitride-Oxide-Silicon (SONOS), Resistive RAM (RRAM), Racetrack, Nano-RAM (NRAM), or Millipede memory. The main memory 122 can be based on any of the above described memory chips, or any other available memory chips capable of operating as described herein. In the implementation shown in FIG. 1C, the processor 121 communicates with main memory 122 via a system bus 150 (described in more detail below). FIG. 1D depicts an implementation of a computing device 100 in which the processor communicates directly with main memory 122 via a memory port 103. For example, in FIG. 1D the main memory 122 can be DRDRAM.

FIG. 1D depicts an implementation in which the main processor 121 communicates directly with cache memory 140 via a secondary bus, sometimes referred to as a backside bus. In other implementations, the main processor 121 communicates with cache memory 140 using the system bus 150. Cache memory 140 typically has a faster response time than main memory 122 and is typically provided by SRAM, BSRAM, or EDRAM. In the implementation shown in FIG. 1D, the processor 121 communicates with various I/O devices 130 via a local system bus 150. Various buses can be used to connect the central processing unit 121 to any of the I/O devices 130, including a PCI bus, a PCI-X bus, or a PCI-Express bus, or a NuBus. For implementations in which the I/O device is a video display 124, the processor 121 can use an Advanced Graphics Port (AGP) to communicate with the display 124 or the I/O controller 123 for the display 124. FIG. 1D depicts an implementation of a computer 100 in which the main processor 121 communicates directly with I/O device 130 b or other processors 121′ via HYPERTRANSPORT, RAPIDIO, or INFINIBAND communications technology. FIG. 1D also depicts an implementation in which local busses and direct communication are mixed: the processor 121 communicates with I/O device 130 a using a local interconnect bus while communicating with I/O device 130 b directly.

A wide variety of I/O devices 130 a-130 n can be present in the computing device 100. Input devices can include keyboards, mice, trackpads, trackballs, touchpads, touch mice, multi-touch touchpads and touch mice, microphones, multi-array microphones, drawing tablets, cameras, single-lens reflex camera (SLR), digital SLR (DSLR), CMOS sensors, accelerometers, infrared optical sensors, pressure sensors, magnetometer sensors, angular rate sensors, depth sensors, proximity sensors, ambient light sensors, gyroscopic sensors, or other sensors. Output devices can include video displays, graphical displays, speakers, headphones, inkjet printers, laser printers, and 3D printers.

Devices 130 a-130 n can include a combination of multiple input or output devices, including, e.g., Microsoft KINECT, Nintendo Wiimote for the WII, Nintendo WII U GAMEPAD, or Apple IPHONE. Some devices 130 a-130 n allow gesture recognition inputs through combining some of the inputs and outputs. Some devices 130 a-130 n provides for facial recognition which can be utilized as an input for different purposes including authentication and other commands. Some devices 130 a-130 n provides for voice recognition and inputs, including, e.g., Microsoft KINECT, SIRI for IPHONE by Apple, Google Now or Google Voice Search.

Additional devices 130 a-130 n have both input and output capabilities, including, e.g., haptic feedback devices, touchscreen displays, or multi-touch displays. Touchscreen, multi-touch displays, touchpads, touch mice, or other touch sensing devices can use different technologies to sense touch, including, e.g., capacitive, surface capacitive, projected capacitive touch (PCT), in-cell capacitive, resistive, infrared, waveguide, dispersive signal touch (DST), in-cell optical, surface acoustic wave (SAW), bending wave touch (BWT), or force-based sensing technologies. Some multi-touch devices can allow two or more contact points with the surface, allowing advanced functionality including, e.g., pinch, spread, rotate, scroll, or other gestures. Some touchscreen devices, including, e.g., Microsoft PIXELSENSE or Multi-Touch Collaboration Wall, can have larger surfaces, such as on a table-top or on a wall, and can also interact with other electronic devices. Some I/O devices 130 a-130 n, display devices 124 a-124 n or group of devices can be augment reality devices. The I/O devices can be controlled by an I/O controller 123 as shown in FIG. 1C. The I/O controller can control one or more I/O devices, such as, e.g., a keyboard 126 and a pointing device 127, e.g., a mouse or optical pen. Furthermore, an I/O device can also provide storage and/or an installation medium 116 for the computing device 100. In still other implementations, the computing device 100 can provide USB connections (not shown) to receive handheld USB storage devices. In further implementations, an I/O device 130 can be a bridge between the system bus 150 and an external communication bus, e.g. a USB bus, a SCSI bus, a FireWire bus, an Ethernet bus, a Gigabit Ethernet bus, a Fibre Channel bus, or a Thunderbolt bus.

In some implementations, display devices 124 a-124 n can be connected to I/O controller 123. Display devices can include, e.g., liquid crystal displays (LCD), thin film transistor LCD (TFT-LCD), blue phase LCD, electronic papers (e-ink) displays, flexile displays, light emitting diode displays (LED), digital light processing (DLP) displays, liquid crystal on silicon (LCOS) displays, organic light-emitting diode (OLED) displays, active-matrix organic light-emitting diode (AMOLED) displays, liquid crystal laser displays, time-multiplexed optical shutter (TMOS) displays, or 3D displays. Examples of 3D displays can use, e.g. stereoscopy, polarization filters, active shutters, or autostereoscopy. Display devices 124 a-124 n can also be a head-mounted display (HMD). In some implementations, display devices 124 a-124 n or the corresponding I/O controllers 123 can be controlled through or have hardware support for OPENGL or DIRECTX API or other graphics libraries.

In some implementations, the computing device 100 can include or connect to multiple display devices 124 a-124 n, which each can be of the same or different type and/or form. As such, any of the I/O devices 130 a-130 n and/or the I/O controller 123 can include any type and/or form of suitable hardware, software, or combination of hardware and software to support, enable or provide for the connection and use of multiple display devices 124 a-124 n by the computing device 100. For example, the computing device 100 can include any type and/or form of video adapter, video card, driver, and/or library to interface, communicate, connect or otherwise use the display devices 124 a-124 n. In one implementation, a video adapter can include multiple connectors to interface to multiple display devices 124 a-124 n. In other implementations, the computing device 100 can include multiple video adapters, with each video adapter connected to one or more of the display devices 124 a-124 n. In some implementations, any portion of the operating system of the computing device 100 can be configured for using multiple displays 124 a-124 n. In other implementations, one or more of the display devices 124 a-124 n can be provided by one or more other computing devices 100 a or 100 b connected to the computing device 100, via the network 104. In some implementations software can be designed and constructed to use another computer's display device as a second display device 124 a for the computing device 100. For example, in one implementation, an Apple iPad can connect to a computing device 100 and use the display of the device 100 as an additional display screen that can be used as an extended desktop. One ordinarily skilled in the art will recognize and appreciate the various ways and implementations that a computing device 100 can be configured to have multiple display devices 124 a-124 n.

Referring again to FIG. 1C, the computing device 100 can comprise a storage device 128 (e.g. one or more hard disk drives or redundant arrays of independent disks) for storing an operating system or other related software, and for storing application software programs such as any program related to the software 120 for the centralized state processing system. Examples of storage device 128 include, e.g., hard disk drive (HDD); optical drive including CD drive, DVD drive, or BLU-RAY drive; solid-state drive (SSD); USB flash drive; or any other device suitable for storing data. Some storage devices can include multiple volatile and non-volatile memories, including, e.g., solid state hybrid drives that combine hard disks with solid state cache. Some storage device 128 can be non-volatile, mutable, or read-only. Some storage device 128 can be internal and connect to the computing device 100 via a bus 150. Some storage device 128 can be external and connect to the computing device 100 via a I/O device 130 that provides an external bus. Some storage device 128 can connect to the computing device 100 via the network interface 118 over a network 104, including, e.g., the Remote Disk for MACBOOK AIR by Apple. Some client devices 100 may not require a non-volatile storage device 128 and can be thin clients or zero clients 102. Some storage device 128 can also be used as an installation device 116, and can be suitable for installing software and programs. Additionally, the operating system and the software can be run from a bootable medium, for example, a bootable CD, e.g. KNOPPIX, a bootable CD for GNU/Linux that is available as a GNU/Linux distribution from knoppix.net.

Client device 100 can also install software or application from an application distribution platform. Examples of application distribution platforms include the App Store for iOS provided by Apple, Inc., the Mac App Store provided by Apple, Inc., GOOGLE PLAY for Android OS provided by Google Inc., Chrome Webstore for CHROME OS provided by Google Inc., and Amazon Appstore for Android OS and KINDLE FIRE provided by Amazon.com, Inc. An application distribution platform can facilitate installation of software on a client device 102. An application distribution platform can include a repository of applications on a server 106 or a cloud 108, which the clients 102 a-102 n can access over a network 104. An application distribution platform can include application developed and provided by various developers. A user of a client device 102 can select, purchase and/or download an application via the application distribution platform.

Furthermore, the computing device 100 can include a network interface 118 to interface to the network 104 through a variety of connections including, but not limited to, standard telephone lines LAN or WAN links (e.g., 802.11, T1, T3, Gigabit Ethernet, Infiniband), broadband connections (e.g., ISDN, Frame Relay, ATM, Gigabit Ethernet, Ethernet-over-SONET, ADSL, VDSL, BPON, GPON, fiber optical including FiOS), wireless connections, or some combination of any or all of the above. Connections can be established using a variety of communication protocols (e.g., TCP/IP, Ethernet, ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), IEEE 802.11a/b/g/n/ac CDMA, GSM, WiMax and direct asynchronous connections). In one implementation, the computing device 100 communicates with other computing devices 100′ via any type and/or form of gateway or tunneling protocol e.g. Secure Socket Layer (SSL) or Transport Layer Security (TLS), or the Citrix Gateway Protocol manufactured by Citrix Systems, Inc. of Ft. Lauderdale, Fla. The network interface 118 can comprise a built-in network adapter, network interface card, PCMCIA network card, EXPRESSCARD network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing the computing device 100 to any type of network capable of communication and performing the operations described herein.

A computing device 100 of the sort depicted in FIGS. 1B and 1C can operate under the control of an operating system, which controls scheduling of tasks and access to system resources. The computing device 100 can be running any operating system such as any of the versions of the MICROSOFT WINDOWS operating systems, the different releases of the Unix and Linux operating systems, any version of the MAC OS for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, any operating systems for mobile computing devices, or any other operating system capable of running on the computing device and performing the operations described herein. Typical operating systems include, but are not limited to: WINDOWS 2000, WINDOWS Server 2012, WINDOWS CE, WINDOWS Phone, WINDOWS XP, WINDOWS VISTA, and WINDOWS 7, WINDOWS RT, and WINDOWS 8 all of which are manufactured by Microsoft Corporation of Redmond, Wash.; MAC OS and iOS, manufactured by Apple, Inc. of Cupertino, Calif.; and Linux, a freely-available operating system, e.g. Linux Mint distribution (“distro”) or Ubuntu, distributed by Canonical Ltd. of London, United Kingdom; or Unix or other Unix-like derivative operating systems; and Android, designed by Google, of Mountain View, Calif., among others. Some operating systems, including, e.g., the CHROME OS by Google, can be used on zero clients or thin clients, including, e.g., CHROMEBOOKS.

The computer system 100 can be any workstation, telephone, desktop computer, laptop or notebook computer, netbook, ULTRABOOK, tablet, server, handheld computer, mobile telephone, smartphone or other portable telecommunications device, media playing device, a gaming system, mobile computing device, or any other type and/or form of computing, telecommunications or media device that is capable of communication. The computer system 100 has sufficient processor power and memory capacity to perform the operations described herein. In some implementations, the computing device 100 can have different processors, operating systems, and input devices consistent with the device. The Samsung GALAXY smartphones, e.g., operate under the control of Android operating system developed by Google, Inc. GALAXY smartphones receive input via a touch interface.

In some implementations, the computing device 100 is a gaming system. For example, the computer system 100 can comprise a PLAYSTATION 3, or PERSONAL PLAYSTATION PORTABLE (PSP), or a PLAYSTATION VITA device manufactured by the Sony Corporation of Tokyo, Japan, a NINTENDO DS, NINTENDO 3DS, NINTENDO WII, or a NINTENDO WII U device manufactured by Nintendo Co., Ltd., of Kyoto, Japan, an XBOX 360 device manufactured by the Microsoft Corporation of Redmond, Wash.

In some implementations, the computing device 100 is a digital audio player such as the Apple IPOD, IPOD Touch, and IPOD NANO lines of devices, manufactured by Apple Computer of Cupertino, Calif. Some digital audio players can have other functionality, including, e.g., a gaming system or any functionality made available by an application from a digital application distribution platform. For example, the IPOD Touch can access the Apple App Store. In some implementations, the computing device 100 is a portable media player or digital audio player supporting file formats including, but not limited to, MP3, WAV, M4A/AAC, WMA Protected AAC, AIFF, Audible audiobook, Apple Lossless audio file formats and .mov, .m4v, and .mp4 MPEG-4 (H.264/MPEG-4 AVC) video file formats.

In some implementations, the computing device 100 is a tablet e.g. the IPAD line of devices by Apple; GALAXY TAB family of devices by Samsung; or KINDLE FIRE, by Amazon.com, Inc. of Seattle, Wash. In other implementations, the computing device 100 is an eBook reader, e.g. the KINDLE family of devices by Amazon.com, or NOOK family of devices by Barnes & Noble, Inc. of New York City, N.Y.

In some implementations, the communications device 102 includes a combination of devices, e.g. a smartphone combined with a digital audio player or portable media player. For example, one of these implementations is a smartphone, e.g. the IPHONE family of smartphones manufactured by Apple, Inc.; a Samsung GALAXY family of smartphones manufactured by Samsung, Inc.; or a Motorola DROID family of smartphones. In yet another implementation, the communications device 102 is a laptop or desktop computer equipped with a web browser and a microphone and speaker system, e.g. a telephony headset. In these implementations, the communications devices 102 are web-enabled and can receive and initiate phone calls. In some implementations, a laptop or desktop computer is also equipped with a webcam or other video capture device that enables video chat and video call.

In some implementations, the status of one or more machines 102, 106 in the network 104 are monitored, generally as part of network management. In one of these implementations, the status of a machine can include an identification of load information (e.g., the number of processes on the machine, CPU and memory utilization), of port information (e.g., the number of available communication ports and the port addresses), or of session status (e.g., the duration and type of processes, and whether a process is active or idle). In another of these implementations, this information can be identified by a plurality of metrics, and the plurality of metrics can be applied at least in part towards decisions in load distribution, network traffic management, and network failure recovery as well as any aspects of operations of the present solution described herein. Aspects of the operating environments and components described above will become apparent in the context of the systems and methods disclosed herein.

B. Multi-Factorial Real-Time Predictive Model for Healthcare Events

Systems and methods in accordance with present implementations can generate opportunities for consuming healthcare products and services based on detecting and analyzing various promotions, prices, accounts, or product offerings by a number of healthcare support services. Present implementations can analyze particular promotions, prices, accounts, or product offerings linked to a healthcare participant and generate comparisons for opportunities for interacting with those promotions, prices, accounts, or product offerings with an existing pattern of consumption of a healthcare service or a pattern of product consumption individualized to that user. Present implementations can thus include a participant condition validator to analyze one or more health states, for example, associated with an individual participant of one or more health systems, services, or any combination thereof, for example, an opportunity aggregation engine to generate aggregated metrics associated with various individual opportunities and individual participants, and a service optimization engine to generate metrics relevant to various individual opportunities and individual participants, and to model and compare any combination of current, existing, optimized, and aggregated models for the various individual opportunities associated with the individual participants.

Referring now to FIG. 2, a block diagram depicting an implementation of a system 200 comprising a centralized state processing system is shown. In brief overview, the system 200 includes an data processing system 120 (“DPS”) that can receive and/or transmit data via a network 104 with participant computing devices 232 a-n, third-party administrator devices 240 a-n, employer devices 238 a-n, point-of-sale terminals 236 a-n, or heterogeneous electronic funding sources 234 a-n. The DPS 120 can include a communications interface 204 that is configured with one or more communications ports, application programming interfaces, network protocols (e.g., TCP/IP), authentication protocols, or security protocols (e.g., SSL). The DPS 120 can include, interface with, or otherwise access an event control engine 310 that controls generation, display, and the like, of opportunity objects and participant objects. The DPS 120 can include, interface with, or otherwise access an predictive service engine 320 that validates, aggregates, optimizes, and the like, opportunity objects according to one or more metrics associated with at least one of the database 330, the third party administrator devices 240, and the participant computing devices 232. The DPS 120 can include one or more databases or data structures that store information to facilitate the systems and methods of the present solution, such as database 330. The database 330 can include data structures, files or otherwise categorize information into different databases based at least partially by object. The database 330 can also include one or more policies, profiles, merchant information, or historical transaction activity, and the like.

The system 120, event control engine 310, and predictive service engine 320 can each include one or more processing units or other logic devices such as programmable logic array engines, modules, or circuitry designed and constructed to facilitate managing security on a network infrastructure. The DPS 120 can include the components 100 shown in FIG. 1C or FIG. 1D, or be configured to operate as a service in cloud 108. The DPS 120 can include or interact with one or more servers 106 a-n and clients 102 a-n. The participant computing devices 232 a-n, POS terminals 236 a-n, employer devices 238 a-n, TPA devices 240 a-n, or heterogeneous electronic funding sources 234 a-n can each include one or more component or functionality of client computing devices 102 a-n or server 106 a-n.

In some implementations, the DPS 120 can employ a multitier architecture such as a client-server architecture in which presentation, application processing, and data management functions are logically or physically separated. The presentation tier, or front-end, can include the communications interface 204 that can serve static content or dynamic content to be rendered by the client 102 (e.g., by a web browser executing on client 102). The presentation tier or web server can interact or communicate with the application tier to obtain data to provide to the client 102, computing devices, 232 a-n, TPA devices 240 a-n, employer devices 238 a-n, funding sources 234 a-n, or POS terminals 120 a-n. The application tier can include the event control engine 310 and the predictive service engine 320. The application tier can interact with the data tier to obtain the transaction data. The data tier can include data persistence mechanisms (database servers, file shares, etc.) and the data access layer that encapsulates the persistence mechanisms and exposes the data. The data tier can include databases 214. The data tier can include an application programming interface (API) to the application tier. The databases 214 can include stored procedures (e.g., SQL statements) that perform tasks with respect the stored data.

The system 200 can include, access, or otherwise communicate with one or more third-party administrator (“TPA”) devices 240 a-n. A TPA can refer to an organization that processes insurance claims or certain aspects of employee benefit plans for a separate entity. A TPA can refer to organizations within the insurance industry which “administer” other services such as Underwriting or Customer Service. In some cases, a TPA can handle the claims processing for employers 238 a-n that self-insures its employees 232 a-n. Thus, the employers 238 a-n are acting as an insurance company and underwrites the risk. The risk of loss can remains with the employer 238 a-n, and not with the TPA 240 a-n. An insurance company may also use a TPA 240 a-n to manage its claims processing, provider networks, utilization review, or membership functions. The TPA 240 a-n can handle many aspects of other employee benefit plans such as the processing of retirement plans and flexible spending accounts. Many employee benefit plans have highly technical aspects and difficult administration that can make using a specialized entity such as a TPA 240 a-n more cost effective than doing the same processing in house.

In the health care industry, for example, TPAs 240 a-n can administer all or a portion of the claims process. TPAs 240 a-n can be contracted by a health insurer or self-insuring companies to administer services, including claims administration, premium collection, enrollment and other administrative activities. For example, an employer 238 a-n may choose to help finance the health care costs of its employees 232 a-n by contracting with a TPA 240 a-n to administer many aspects of a self-funded health care plan.

Administrators, such as companies or health insurance providers, can establish electronic benefits accounts such as flexible spending accounts or healthcare tax benefit accounts (e.g., health savings accounts) for participants such as employees, subscribers, or customers. These electronic benefits accounts can provide a tax advantage for the participants. Administrators that establish or provide electronic tax benefits accounts for various participants of those accounts can utilize backend information technology infrastructure to process, analyze, monitor or manage the electronic tax benefits accounts. The tax benefit management information technology infrastructure can be configured with processing rules that are applied to electronic transactions. Electronic transactions can include allocating funds to the tax benefit account, withdrawing funds from the tax benefit account, making a purchase with funds from the tax benefit account, modifying a profile of the tax benefit account, or submitting a claim. The management information technology infrastructure can apply one or more rules to each type of transaction to determine an event. As the types of transactions and rules increase in number and complexity, the types and events can also increase in number and complexity, thereby consuming an increasing amount of resources of the information technology infrastructure. For example, events such as a card denial increases the number of transaction attempts, communications with the server, account resets, profile corruption, or resources consumed by a point-of-sale device initiating the transaction.

The employer devices 238 a-n can refer to a device used by an entity or organization that is associated with the participant computing devices 232 a-n of the employees of the employer. For example, Employer A can be a software company that has a thousand employees associated with the participant computing devices 232 a-n. The employees can obtain health care or other services, and pay for those services at a POS terminal 236 a-n of the service provider.

Data packets can be generated by a device 240 a-n at an administrator. The device can refer to an administrator device (“administrator device”) such as administrator device 240 a-n. The administrator device 240 a-n may monitor data from the various electronic benefits accounts associated with the administrator. The accounts associated with the administrator may be accounts that are managed or maintained by the administrator. The administrator may be a point of contact for customers or participants of the associated accounts. In some implementations, the client 102, which may correspond to an individual participant of the administrator's electronic benefits account, may access the account and perform a number of actions with respect to the account, such as, fund the account (e.g., via heterogeneous electronic funding sources 234 a-n), withdraw from the account, charge the account, and the like. The administrator of the electronic benefits account, as the caretaker of the account, may adjust parameters associated with the account, such as, monthly fees, minimum running balances, etc. At the same time, the DPS 120 may monitor the data, parameters, and performance of the account and store the information under an administrator profile associated with the administrator of the account. The DPS 120 may receive the data associated with the individual participants and their individual accounts from the client's 102 and the parameter data associated with the accounts from the administrator device 240 a-n via the network 104.

An administrator device 240 a-n can be the place where an administrator may perform various functions of the administrator, for example, functions associated with electronic benefits accounts of the administrator. The administrator device 240 a-n is the point at an administrator that may send requests or transaction information to the DPS 120 for further processing or data collection. The administrator device 240 a-n may also be configured to transmit an identifier associated with the administrator corresponding to the administrator device 240 a-n for identification by the DPS 120. In some implementations, the receiving of the identifier initiates a fraud detection and control process.

The administrator device 240 a-n can include hardware and software. Administrators can utilize scanners, EFTPOS terminals, touch screens and any other wide variety of hardware and software available for use with administrator device 240 a-n. For example, an administrator can use software to make adjustments to parameters associated with their electronic benefits accounts.

The administrator device 240 a-n can include advanced features to cater to different functionality, such as account participant forecasts and estimates, account simulation, communication with participants of accounts, performing actions associated with individual participant accounts (e.g., freezing an account), collecting data from one or more of the participant accounts, etc., all built into the administrator software. The administrator device 240 a-n can be configured to execute user-input commands with respect to the electronic benefits accounts of the administrator.

In some implementations, the communication interface 204 can receive data packets. The data packets can carry one or more electronic transactions. In some cases, the data packets can carry multiple electronic transactions. In some cases, the data packets can be received over a duration of time. The electronic transactions can occur over the duration of time. In some cases, the electronic transaction information carried via the data packets can be received by the DPS 120 in real-time, such as responsive to the occurrence of the electronic transaction. In some cases, the DPS 120 can receive the information about the electronic transactions in a bulk upload or batch upload. Receiving the information about the electronic transaction sin a bulk upload or batch upload can reduce computing resource utilization or network bandwidth usage, thereby improving the efficiency of the DPS 120. For example, the provider of the information about the electronic transactions can compress the information and generate data packets carrying the compressed information in a single batch or bulk transmission, thereby reducing network bandwidth utilization.

The electronic transaction information carried via the data packets can include information that facilitates performance of the electronic transaction, or analyzing the electronic transaction to detect fraudulent activity. The electronic transaction can a source identifier pointing to a data structure storing a resource, a destination identifier corresponding to a data structure to transfer the resource, and an intermediary identifier corresponding to an entity that provides at least a portion of the resource stored in the data structure. The source identifier can refer to an account identifier that contains the resource being transferred from a source to a destination. The source identifier can refer to an account of an employee of an employer. The source identifier can correspond to an account associated with a participant computing device 232. The resource can correspond to an electronic resource or physical resource being represented in an electronic form. The resource can refer to or include a token, currency, points, or other resource that can be transferred from the source to a destination. The destination identifier an correspond to an entity or organization receiving the resource. The destination identifier can correspond to a provider of a service or good that is receiving the resource in return for performing the service or providing the good to the employee. The intermediary identifier can correspond to an entity that stores, holds, manages, provides, or maintains the resource. The intermediary identifier can refer to or correspond to the employer device 238 a-n, a heterogeneous electronic funding source 234 a-n or TPA 240 a-n.

An identifier corresponding to a data structure can refer to or include an identifier pointing to a data structure, such as a memory pointer. The identifier corresponding to a data structure can refer to or include an identifier used by a lookup to retrieve, identify, access or select the data structure. The identifier can label the data structure. The identifier can be mapped to the data structure.

The data packets or electronic transaction can be generated by a device at a merchant to conduct an electronic transaction at the merchant. The device can refer to a point of sale terminal (“POS terminal”) such as POS terminal 236 a-n. In some implementations, the POS terminals 236 a-n are the devices at which retail transactions are initiated. The POS terminals 236 a-n are the points at which a customer of the entity or merchant makes a payment to the merchant in exchange for goods or services. At the point of sale the merchant can calculate the amount owed by the customer and provide options for the customer to make payment. The merchant can also issue a receipt for the transaction.

The POS terminal 236 a-n can include hardware and software. Merchants can utilize weighing scales, scanners, electronic and manual cash registers, EFTPOS terminals, touch screens and any other wide variety of hardware and software available for use with POS terminal 236 a-n. For example, a pharmacy can use software to customize the item or service sold when a customer has a special medication request.

The POS terminal 236 a-n can include advanced features to cater to different functionality, such as inventory management, CRM, financials, warehousing, flexible spending account transactions, etc., all built into the POS software. The POS terminal 236 a-n can be configured to conduct a transactions using a debit card, multipurse card, Bluetooth, near field communications, smartphone, smartwatch, mobile telecommunications computing device, wearable communications, RFID, etc.

The DPS 120 can include a communications interface 204. The communications interface 204 can execute on one or more processors of a server. The communications interface 204 can include one or more communications ports and be configured with one or more network protocols. Communications ports can include, e.g., network ports, Ethernet ports, WAN ports, I/O ports, or software ports. The communication port can be configured with a network protocol such as Transport Layer Protocols such as TCP/IP or UDP that are configured to receive and process data packets received via a computer network. The port can include or be associated with an IP address of a host and a protocol type of the communication.

The communications interface 204 can receive data packets generated by the POS terminal 236 a-n responsive to an electronic transaction resulting in transmission of a request to adjudicate a single claim against an electronic benefits account. In some implementations, the request to adjudicate a single claim against the electronic benefits account is transmitted responsive to a user swiping a payment card at the POS terminal. The payment card can include identifying information that can be used to identify an account identifier of the electronic benefit account (e.g., source identifier) against which to adjudicate the claim. The data packets can include header information and payload information. Multiple data packets can be strung together in a sequence. The header information can refer to TCP/IP headers that include fields such as source port, destination port, sequence number, acknowledge number, window size, etc. The payload information of the data packet can include information related to the electronic transaction, the request to adjudicate a single claim, the merchant, or the customer. The DPS 120 can receive the data packet with header information and payload information and process the packets to obtain information for further processing. The payload can include data identifying the POS terminal 236 a-n (e.g., POS terminal 236 a) at which the electronic transaction occurred, the merchant providing the POS terminal 236 a, a merchant category of the merchant, financial information associated with the user performing the electronic transaction (e.g., via a card swipe or other communication technique used to perform the electronic transaction), an amount of expenditures of the electronic transaction, and other information facilitating adjudication of the single claim. The data packets (e.g., via the payload) can include the request to adjudicate the single claim. The request can specify the electronic benefits account for adjudication. The request can specify information for identifying a policy for performing the adjudication. The payload can include data identifying a merchant category of the merchant, an electronic benefits account, and a monetary amount of the electronic transaction.

The data packets can carry data identifying a merchant or merchant category of the merchant. In some implementations, the data carried by the data packets include a merchant category code or identifier (e.g., dental, medical, etc.). In some implementations, the data identifies a merchant, and the DPS 120 determines a merchant category based on the identification of the merchant by, for example, using a merchant to merchant category mapping or lookup table stored in database. In some implementations, the data packets carrying the request to adjudicate the single claim against the electronic benefits account include a data structure having a first field indicating a merchant identifier, a second field indicating a total amount of expenditures, and a third field indicating the electronics benefit account. In some implementations, the data packets are generated by a merchant device (e.g., a client device 102 of a merchant) to conduct an electronic transaction at the merchant, and the data packets carry data identifying a merchant category of the merchant, the electronic benefits account maintained and configured on the DPS 120, and a total monetary amount of the electronic transaction.

The data packets (e.g., payload of the data packets) can further identify an electronic account maintained and configured on the server. The electronic account can be maintained and configured in a database 214. The electronic account can correspond to a user and have a unique identifier. The unique identifier can include numbers, letters, characters, symbols, etc. The electronic account can be associated with the customer making the transaction at the merchant. The POS terminal 236 a can receive or determine the electronic account identifier via a card swipe or other communication technique employed at the POS terminal 236 a, which the POS 236 a can then convey to the DPS 120.

The communications interface 120 can further receive data packets (e.g., payload information) identifying a monetary amount of the electronic transaction, such as a total amount of expenditures. The monetary amount can be for the purchase of goods or services made at the merchant. The monetary amount of the transaction can refer to the amount of funds in consideration for goods or services obtained from the entity or merchant. The merchant or entity can refer to the entity at which a point-of-sale terminal or device used to make the transaction is located or with which the terminal is associated. The monetary amount can be in any currency (e.g., United States dollars) or units. The monetary amount can be further tied to a category, such as medical services.

In some implementations, the POS terminal 236 a can generate multiple data packets for a single transaction. The multiple data packets can each include a header and a payload. The header can indicate that the multiple data packets are to be grouped together for routing, transmission or processing purposes.

FIG. 3 illustrates an example data processing system, in accordance with present implementations. In some implementations, the example data processing system (DPS) 300 can correspond to DPS 120. The DPS 300 can include at least one event control engine 310. The DPS 300 can include at least one predictive service engine 320. The DPS 300 can include at least one database 330. The event control engine 310 can include an event message controller 312, a health service gateway 314, a participant gateway 316, and a system scheduler 318. The predictive service engine 320 includes participant condition validator 322, an opportunity aggregation engine 324, and a service optimization engine 326.

The event control engine 310 can generate, modify, block, transmit, and the like, communication messages between at least the participant computing devices 232, the third party administrator device 240, the predictive service engine 320, and the database 330. The event control engine 310 includes or is associated with one or more operating systems, virtual machines, interpreters, or any combination thereof, for example, to generate, modify, block, transmit, or any combination thereof, for example, the communication messages. The communication messages are or include commands formatted as at least one GET request, PUT request, or any combination thereof, for example. The event control engine 310 includes one or more logical or electronic devices including but not limited to integrated circuits, logic gates, flip flops, gate arrays, programmable gate arrays, and the like. It is to be understood that any electrical, electronic, or like devices, or components associated with the event control engine 310 can also be associated with, integrated with, integrable with, replaced by, supplemented by, complemented by, or any combination thereof, for example, a system processor or any component thereof.

The event message controller 312 can restrict, regulate, modify, block, transmit, or any combination thereof, for example, one or more communication messages according to one or more transmission criteria or security criteria. The event message controller 312 enforces one or more security policies including one or more security criteria. The event message controller 312 applies a first security policy to communication messages associated with the participant communication devices 232. The first security policy can include a user level trust zone, associated with a low trust level. The event message controller 312 applies a second security policy to communication messages associated with the third party administrator devices 240. The second security policy can include a third party level trust zone, associated with a low trust level. The event message controller 312 applies a third security policy to communication messages associated with the event control engine 310 and the predictive service engine 320. The third security policy can include a cloud trust zone, associated with a medium trust level. It is to be understood that various security policies and various trust zones include, but are not limited to varying numbers and degrees of security encapsulation or any combination thereof, for example. The security encapsulation includes token validation, password validation, hardware key validation, symmetric encryption key validation, and asymmetric encryption key validation.

The health service gateway 314 can generate one or more communication messages compatible with one or more of the third party administrator devices, based on communication messages received from the event message controller 312. The health service gateway 314 is further operable to generate one or more communication messages compatible with the event message controller 312, based on communication messages received from one or more of the third party administrator devices 240. The health service gateway can include at least one application programming interface (API) compatible with the third party administrator devices 240 and the event message controller 312. The health service gateway 314 can include at least one API to interface with third party administrator devices 240 associated with multiple third party administrator services. As one example, a third party administrator service can be a participant's health insurance service or a participant's prescription drug records service. The second security policy is configured to securely couple the event control engine 310 to the third party administrator devices by the health service gateway 314, in accordance with one or more health records standards. As one example, health records standards may be associated with Health Insurance Portability And Accountability Act (HIPAA) requirements or any combination thereof, for example.

The participant gateway 316 can generate one or more communication messages compatible with one or more of the participant computing devices 232, based on communication messages received from the event message controller 312. The participant gateway 316 is further operable to generate one or more communication messages compatible with one or more of the participant computing devices 232, based on communication messages received from the event message controller 312. The participant gateway 316 can include at least one API compatible with the participant computing devices 232 and the event message controller 312. The participant gateway 316 is configured to encapsulate at least one opportunity object received from the predictive service engine 320 and transmit the encapsulated opportunity object to one or more of the participant computing devices 232. The participant gateway 316 encapsulates an opportunity object in a JSON container, an encrypted package, a compressed package, or any combination thereof, for example. The participant gateway 316 transmits the opportunity object or the encapsulated opportunity object in accordance with the first security policy.

The system scheduler 318 can generate, modify, block, transmit, and the like, communication messages associated with opportunity objects in accordance with at least one transmission schedule or at least one batch processing criterion. The transmission schedule is a synchronous transmission schedule including at least one transmission interval between transmission times. The event control engine 310 transmits, generates, or any combination thereof, for example, one or more communication messages associated with opportunity objects at a transmission time. In some implementations, a batch processing criterion includes a connectivity condition between at least one of the participant computing devices 232 and one or more of the event control engine 310 and the data processing system 120. The system scheduler 318 enters an asynchronous mode in response to determining that connectivity with one or more particular participant computing devices 240 is below a connectivity threshold. The batch processing criterion includes the connectivity threshold. As one example, a connectivity threshold can be a minimum lag, bandwidth, uptime, or any combination thereof, for example, associated with a minimum ability to transmit one or more opportunity objects. The system scheduler 318 includes one or more queues to asynchronously store, buffer, or any combination thereof, for example, any communication messages that cannot be transmitted at a particular transmission time due to failure to satisfy a connectivity threshold or satisfying a batch processing criterion. The system scheduler 318 is selectably configurable into a synchronous mode, an asynchronous mode, or both. The system scheduler 318 is selectably configurable by at least one API associated therewith.

The predictive service engine 320 can validate, aggregate, optimize, and the like, opportunity objects according to one or more metrics associated with at least one of the database 330, the third party administrator devices 240, and the participant computing devices 232. The predictive service engine 320 is operatively coupled to at least one of the participant database 332 and the opportunity database 334 to store, retrieve, modify, generate, link, delete, and the like, one or more objects associated therewith. The predictive service engine 320 is operatively coupled to one or more of the third-party administrator devices 240 by the event control engine 310. The event control engine 310 can effect real-time or substantially real-time communication with one or more secure third party healthcare systems through one or more of the third party administrator devices 240. Real-time or near-real-time communication can include, but is not limited to, reducing or eliminating latency of or delay in communication to below a perceivable level by a user. As one example, latency or delay can be introduced by incompatibilities between third-party computing systems, or databases, and can be reduced or eliminated by interconnection of those systems or databases through automated communication interfaces compatible with both. The predictive service engine 320 includes one or more logical or electronic devices including but not limited to integrated circuits, logic gates, flip flops, gate arrays, programmable gate arrays, and the like. It is to be understood that any electrical, electronic, or like devices, or components associated with the predictive service engine 320 can also be associated with, integrated with, integrable with, replaced by, supplemented by, complemented by, or any combination thereof, for example, a system processor or any component thereof.

The participant condition validator 322 can obtain, compare, filter, match, aggregate, analyze, or any combination thereof, for example, one or more conditions, metrics, characteristics, values, constraints, markers, biomarkers, or any combination thereof, for example, associated with one or more participants. The participant condition validator 322 can obtain secure, private, or like health information associated with a participant in real-time or substantially real-time from at least one of the third party administrator devices 240. The participant condition validator 322 can validate one or more of a participant, a service associated with a participant, and a preference, rating, characterization, or any combination thereof, for example, associated with a service and a participant. The participant condition validator 322 can perform one or more validation operations for one or more of a service and a participant in real-time or substantially real-time.

The opportunity aggregation engine 324 can aggregate one or more conditions, metrics, characteristics, values, constraints, markers, biomarkers, or any combination thereof, for example, associated with a predetermined a service and a predetermined participant. The opportunity aggregation engine 324 can identify one or more health services associated with at least one of a participant and an opportunity associated with the participant. The opportunity aggregation engine 324 can aggregate consumption of a health service over at least one of a time period, usage frequency, physical distance, or any combination thereof, for example.

The service optimization engine 326 can generate one or more predictive opportunity execution models. The service optimization engine 326 can generate at least one predictive opportunity execution model associated with repeated execution of one or more opportunities associated with a participant. The service optimization engine 326 can generate multiple predictive opportunity execution models and compare the models based on one or more metrics common to each model. As one example, the service optimization engine 326 can compare cost of a current prescription fulfilled by a first pharmacy with cost of a current prescription fulfilled by a second pharmacy associated with an opportunity object. In this example, the service optimization engine 326 can compare cost over a particular period of time associated with a prescription drug consumption regime, schedule, or any combination thereof, for example.

FIG. 4 illustrates an example participant database system further to the example data processing system of FIG. 3, in accordance with present implementations. The example participant database system 400 can correspond to the participant database 332. The example participant database system 400 includes at least one of a participant object 410, an insurance object 420, a prescription object 430, a pharmacy object 440, a participant profile object 450, and a drug object 460. It is to be understood that the example participant database system can support an arbitrary number of objects dependent only upon extrinsic characteristics of the example participant database system. As one example, extrinsic characteristics of the example participant database system can include a storage capacity thereof.

The participant object 410 is configured to encapsulate at least one executable, module, link, and the like associated with a participant of a health support system and the like. The participant object includes at least one object identifier 412, timestamp 414, participant key 416, geolocation monitor 418, insurance linker 422, prescription linker 432, pharmacy linker 442, participant profile linker 452, and opportunity object linker 402.

The object identifier 412 contains one or more characteristics associated with a user of the data processing system 120. The object identifier includes one or more of a database key identifying the participant object, a participant identifier associated with a health service, a participant identifier associated with a participant computing device, and the like. As one example, the participant identifier associated with the health service includes at least one an indication of an association between a participant and a particular health service provider, and an association between the participant and a particular health account provider. As another example, the participant identifier associated with the health service includes an indication that the participant is associated with a particular type of health service or health account. As another example, health account type can be an HSA account. The timestamp 414 can indicate one or more times of critical events. The timestamp 414 can include one or more UNIX timestamps. The timestamp 414 includes at least one of a creation timestamp, a modification timestamp. The modification timestamp is responsive to any action to the participant object 410 by the predictive service engine 320 or any component thereof.

The participant key 416 contains a key based on multiple identifiers associated with a participant. The participant key 416 is based on at least one of an account code, an employer identifier and an employee identifier associated with the participant and the health service associated with the participant. The participant key 416 is encrypted, hashed, or any combination thereof, for example. The participant key 416 is available as a validation key for authorization through the first, second or third security policies.

The geolocation monitor 418 contains a geolocation associated with the participant associated with the participant object 410. The geolocation monitor 418 stores one or more latitude and longitude values to designate a geolocation. The geolocation monitor 418 obtains a geolocation from a participant computing device 240 associated with the participant object 410. The geolocation monitor 418 obtains the geolocation by the participant gateway 316. The event control engine 310 can modify the geolocation monitor 418. The geolocation monitor 418 receives a geolocation on a constantly updating basis. The geolocation monitor 418 receives the geolocation on a scheduled basis in response to actions by the system scheduler 318.

The insurance linker 422 contains a dynamic link to the insurance object 420. The insurance linker 422 is operable to generate, store, or any combination thereof, for example, a link, reference, or any combination thereof, for example, to the insurance object 420 associated with the participant object 410. The insurance linker 422 associates the insurance object 420 with the participant object 410 in accordance with one or more of an import process of participant information from one or more third party administrator devices 240, or a selection, interaction, or any combination thereof, for example, received from a participant computing device 232 associated with the participant object 410.

The prescription linker 432 contains a dynamic link to the prescription object 430. The prescription linker 432 is operable to generate, store, or any combination thereof, for example, a link, reference, or any combination thereof, for example, to the prescription object 430 associated with the participant object 410. The prescription linker 432 associates the prescription object 430 with the participant object 410 in accordance with one or more of an import process of participant information from one or more third party administrator devices 240, or a selection, interaction, or any combination thereof, for example, received from a participant computing device 232 associated with the participant object 410.

The pharmacy linker 442 contains a dynamic link to the pharmacy object 440. The pharmacy linker 442 is operable to generate, store, or any combination thereof, for example, a link, reference, or any combination thereof, for example, to the pharmacy object 440 associated with the participant object 410. The pharmacy linker 442 associates the pharmacy object 440 with the participant object 410 in accordance with one or more of an import process of participant information from one or more third party administrator devices 240, or a selection, interaction, or any combination thereof, for example, received from a participant computing device 232 associated with the participant object 410.

The participant profile linker 452 contains a dynamic link to the participant profile object 450. The participant profile linker 452 is operable to generate, store, or any combination thereof, for example, a link, reference, or any combination thereof, for example, to the participant profile object 450 associated with the participant object 410. The participant profile linker 452 associates the participant profile object 450 with the participant object 410 in accordance with one or more of an import process of participant information from one or more third party administrator devices 240, or a selection, interaction, or any combination thereof, for example, received from a participant computing device 232 associated with the participant object 410.

The opportunity object linker 402 contains a dynamic link to an opportunity object 510. The opportunity object linker 402 is operable to generate, store, or any combination thereof, for example, a link, reference, or any combination thereof, for example, to the opportunity object 510 associated with the participant object 410. The participant profile linker 452 associates the participant profile object 450 with the participant object 410 in accordance with one or more of an import process of participant information from one or more third party administrator devices 240, or a selection, interaction, or any combination thereof, for example, received from a participant computing device 232 associated with the participant object 410.

The insurance object 420 is configured to encapsulate at least one executable, module, link, and the like associated with a participant of a health support system and the like. The insurance object is associated with, represents, or any combination thereof, for example, characteristics, variables, executables, or any combination thereof, for example, associated with a health support account, a support account, or any combination thereof, for example. The insurance object 420 is configured to execute to facilitate execution of one or more instructions to a health support account, support account, or any combination thereof, for example. The insurance object 420 can include an object identifier 424 and a provider encapsulator 426. The object identifier 424 contains one or more characteristics associated with an insurance entity associated with the participant object 410 of the data processing system 120. The object identifier 424 contains or can include one or more blocks, links, executables, or any combination thereof, for example, associated with at least one of the participant object 410 or a participant associated with the participant object 410. The provider encapsulator 426 is configured to encapsulate at least one executable, module, link, and the like associated with a health support system and the like. The object identifier 424 can include one or more identifiers associated with a health support account. As one example, the object identifier 424 can include one or more of a health insurance carrier identification block, a health insurance group identification block, and a health insurance UUID identification block.

The prescription object 430 is configured to encapsulate at least one executable, module, link, and the like associated with a pharmaceutical system, pharmaceutical entity, pharmaceutical account, pharmaceutical, health metric, biological metric, or any combination thereof, for example, associated with the participant object 410 or a participant associated with the participant object 410. The prescription object 430 can include an object identifier 434, a prescription encapsulator 436, a participant key 416, a drug linker 462, and a pharmacy linker 442. The prescription object can include corresponding instances, references, or any combination thereof, for example, of one or more elements or components associated with corresponding objects of the example participant database system. The object identifier 434 contains one or more characteristics associated with a pharmaceutical entity associated with the participant object 410 of the data processing system 120. The object identifier 434 contains or can include one or more blocks, links, executables, or any combination thereof, for example, associated with at least one of the participant object 410 or a participant associated with the participant object 410.

The prescription encapsulator 436 is configured to encapsulate at least one executable, module, link, and the like associated with at least one quantity, metric, volume, amount, mass, time, dosage, and the like of at least one drug object 460. The prescription encapsulator is configured to encapsulate at least one personally-identifiable health record within a secure wrapper. The prescription object 430 is configured to securely encapsulate at least one record of any type with the participant key 416. The participant key 416 can decrypt, either alone or in combination with additional security components, the prescription encapsulator 436 or any wrapper, for example, associated therewith.

The participant key 438 contains a key based on multiple identifiers associated with a participant. The participant key 438 can correspond to the participant key 416. The participant key 438 is a copy, reference, link, or any combination thereof, for example, to the participant key 416. The prescription object is configured to validate or facilitate validation of information, executables, parameters, or any combination thereof, for example, contained in or associated with the participant object 410, the insurance object 420, or the prescription object 430.

The drug linker 462 contains a dynamic link to the drug object 460. The drug linker 462 is operable to generate, store, or any combination thereof, for example, a link, reference, or any combination thereof, for example, to the drug object 460 associated with the prescription object 430. The drug linker 462 associates the prescription object 430 with the drug object 460 in accordance with one or more of an import process of participant information from one or more third party administrator devices 240, or a selection, interaction, or any combination thereof, for example, received from a participant computing device 232 associated with the participant object 410. The drug linker 462 generates a link, reference, or any combination thereof, for example, to at least one drug object 460 within a prescription associated with a participant object 410. As one example, the drug linker 462 can generate and maintain a link to a drug object statically or dynamically based on a drug provider by a health support service, support service, or any combination thereof, for example.

The pharmacy linker 442 contains a dynamic link to the pharmacy object 440. The pharmacy linker 442 is operable to generate, store, or any combination thereof, for example, a link, reference, or any combination thereof, for example, to the pharmacy object 440 associated with the prescription object 430. The pharmacy linker 442 associates the prescription object 430 with the pharmacy object 440 in accordance with one or more of an import process of participant information from one or more third party administrator devices 240, or a selection, interaction, or any combination thereof, for example, received from a participant computing device 232 associated with the participant object 410. The pharmacy linker 442 generates a link, reference, or any combination thereof, for example, to at least one pharmacy object 440 within at least one pharmacy selection criterion associated with a participant object 410. As one example, the pharmacy linker 440 can generate and maintain a link to a pharmacy object statically or dynamically based on a restriction or preference selection by a participant, a restriction or preference by a health support service, support service, or any combination thereof, for example.

The pharmacy object 440 is configured to encapsulate at least one executable, module, link, and the like associated with a pharmacy system, pharmacy entity, pharmacy account, health metric, consumer metric, or any combination thereof, for example, associated with the participant object 410 or a participant associated with the participant object 410. The pharmacy object 440 can include an object identifier 444, a location identifier 446, and a geolocation identifier 448. The object identifier 444 contains one or more characteristics associated with a pharmacy associated with the participant object 410 of the data processing system 120. The object identifier 444 can include one or more blocks, links, executables, or any combination thereof, for example, associated with at least one of the participant object 410 or a participant associated with the participant object 410. The location identifier 446 contains one or more characteristics associated with a pharmacy location associated with the participant object 410 of the data processing system 120. The location identifier 446 can include one or more addresses, pharmacy location codes, phone numbers, and the like. The geolocation identifier 448 contains one or more characteristics associated with a pharmacy geolocation associated with the participant object 410 of the data processing system 120. The location identifier 446 can include one or more latitude coordinates, longitude coordinates, spherical coordinates, Global Positioning System (“GPS”) coordinates, pathfinding routes, and the like.

The participant profile object 450 is configured to encapsulate at least one executable, module, link, and the like associated with a health metric, consumer metric, diagnostic assessment, prognostic assessment, health device interface, or any combination thereof, for example, associated with the participant object 410 or a participant associated with the participant object 410. The participant profile object can include an object identifier 454 and a diagnostic encapsulator 456. The object identifier 454 contains one or more characteristics associated with a pharmacy associated with the participant object 410 of the data processing system 120. The object identifier 454 can include one or more blocks, links, executables, or any combination thereof, for example, associated with at least one of the participant object 410 or a participant associated with the participant object 410.

The diagnostic encapsulator 456 is configured to encapsulate at least one executable, module, link, and the like associated with at least one characteristic of the participant object 410 or a participant associated with the participant object 410. As one example, diagnostic encapsulator 456 can include at least one of medical history, prescription history, current medical conditions, family medical history, evaluation charts, and the like associated with a participant object 410 or a participant associated with the participant object 410. The diagnostic encapsulator 456 is configured to encapsulate at least one personally-identifiable health record within a secure wrapper. The participant profile object 430 is configured to securely encapsulate at least one record of any type with at least one of the participant key 416 and 438. In some implementations, at least one of the participant key 416 and 438 can decrypt, either alone or in combination with additional security components, the prescription encapsulator 436 or any wrapper, for example, associated therewith. The diagnostic encapsulator 456 is configured to secure information associated therewith or contained therein in accordance with one or more diagnostic security protocols. As one example a diagnostic security protocol can include a restriction on access, decryption, or any combination thereof, for example, based on a HIPAA or like restriction.

The drug object 460 is configured to encapsulate at least one executable, module, link, and the like associated with a drug, pharmaceutical, chemical, biochemical, composition, substance, element, molecule, or any combination thereof, for example, associated with at least one of a pharmacy object 440 and a prescription object 430. The drug object 460 is generated based on a database system, inventory system, sales system, or any combination thereof, for example, associated with at least one of a pharmacy system, health support system, health insurance provider, and the like. As one example, the drug object 460 is generated based on available drugs at one or more pharmacies associated with the participant and included in at least one prescription object associated with the participant. As another example, the drug object 460 is generated based on the diagnostic encapsulator to include drugs associated with a particular health condition of a participant associated with the participant object 410. The drug object can include an object identifier 464 and a drug encapsulator 466. The object identifier 464 contains one or more characteristics associated with a drug object associated with at least one of the participant object 410 and the pharmacy object 440. The object identifier 454 can include one or more blocks, links, executables, or any combination thereof, for example, associated with at least one of the participant object 410 or a participant associated with the participant object 410.

The drug encapsulator 466 is configured to encapsulate at least one executable, module, link, and the like associated with at least one characteristic of the drug object 460. As one example, the drug encapsulator 466 can include at least one of a chemical composition, commercial product identifier, brand identifier, generic product identifier, and the like. The drug encapsulator 466 is configured to encapsulate at least one personally-identifiable health record within a secure wrapper. The drug object 460 is configured to securely encapsulate at least one record of any type with at least one of the participant key 416 and 438. In some implementations, at least one of the participant key 416 and 438 can decrypt, either alone or in combination with additional security components, the drug encapsulator 466 or any wrapper, for example, associated therewith. The drug encapsulator 466 is configured to secure information associated therewith or contained therein in accordance with one or more diagnostic security protocols. As one example a diagnostic security protocol can include a restriction on access, decryption, or any combination thereof, for example, based on a HIPAA or like restriction.

FIG. 5 illustrates an example opportunity database system further to the example data processing system of FIG. 3, in accordance with present implementations. The example opportunity database system 500 can correspond to the opportunity database 334. The example opportunity database system 500 can include at least one of an opportunity object 510, an opportunity type object 520, an opportunity action 530, a prescription action object 540, and an account action object 550.

The opportunity object 510 is configured to encapsulate at least one executable, module, link, and the like associated with a participant of a health support system and the like. The opportunity object 510 is associated with, represents, or any combination thereof, for example, characteristics, variables, executables, or any combination thereof, for example, associated with a health support account, a support account, or any combination thereof, for example. The opportunity object 510 is configured to execute to facilitate execution of one or more instructions to a health support account, support account, or any combination thereof, for example. The opportunity object 510 is configured to execute a financial transaction across a plurality of secure and heterogeneous database system or commercial computing systems. As one example, the opportunity object 510 is configured to execute a transaction with a pharmacy system to obtain a pharmaceutical prescription, including secure patient information and secure financial information of a participant associated with a participant object 410. In this example, the opportunity object 510 achieves the technical solution of secure digital communication across multiple secure systems involving both financial security and patient care security protocols. The opportunity object 510 can include an object identifier 512, a type linker 522, and an action linker 532. The object identifier 512 contains one or more characteristics associated with the opportunity object 510. The object identifier 512 can include one or more blocks, links, executables, or any combination thereof, for example, associated with opportunity object 510.

The type linker 522 contains a dynamic link to the opportunity type object 520. The type linker 522 is operable to generate, store, or any combination thereof, for example, a link, reference, or any combination thereof, for example, to the opportunity type object 520 associated with the opportunity object 510. The type linker 522 associates the opportunity object 510 with the opportunity type object 520 in accordance with one or more of an import process of participant information from one or more third party administrator devices 240, or a selection, interaction, or any combination thereof, for example, received from a participant computing device 232 associated with the participant object 410.

The action linker 532 contains a dynamic link to the opportunity action object 530. The action linker 532 is operable to generate, store, or any combination thereof, for example, a link, reference, or any combination thereof, for example, to one or more opportunity action objects 530 associated with the opportunity object 510. The action linker 532 associates the opportunity object 510 with the opportunity action object 530 in accordance with an import process of participant information from one or more third party administrator devices 240 The action linker 532 associates the opportunity object 510 with the opportunity action object 530 in accordance with a selection, interaction, or any combination thereof, for example, received from a participant computing device 232 associated with the participant object 410. The action linker 532 associates the opportunity object 510 with the opportunity action object 530 in accordance with a scheduled updated initiated by a scheduling process or a batching process.

The opportunity type object 520 is configured to encapsulate at least one executable, module, link, and the like associated with a participant of a health support system and the like. The opportunity object 510 is associated with, represents, or any combination thereof, for example, characteristics, variables, executables, or any combination thereof, for example, associated with an opportunity. As one example, the opportunity type object can include at least one value, metric, executable, link, or any combination thereof, for example, associated with a time-sensitive opportunity. The opportunity type object 520 can include an object identifier 522 and a point module 524. The object identifier 522 contains one or more characteristics associated with the opportunity type object 510. The object identifier 522 can include one or more blocks, links, executables, or any combination thereof, for example, associated with opportunity type object 522.

The point module 524 is configured to encapsulate at least one executable, module, link, and the like associated with an execution value of an opportunity object associated with the opportunity type object 520. As one example, the point module 524 can include at least one value, metric, executable, link, or any combination thereof, for example, associated with a quantitative point value for executing the opportunity object 510. The quantitative point value dynamically changes based on one or more opportunity or participant metrics, factors, for example. As one example, the quantitative point value decreases toward zero as a current timestamp approaches a timestamp threshold.

The opportunity action object 530 is configured to encapsulate at least one executable, module, link, and the like to execute or facilitate execution of at least one action based on at least one of the prescription action object 540 and the account action object 550. The opportunity action object 530 can include one or more instructions, restrictions, security policies, interfaces, and the like to execute or facilitate execution of at least one of the prescription action object 540 and the account action object 550. The opportunity action object 530 include an action associated with an application programming interface (“API”) of the data processing system 120. The opportunity action objects is associated with an API call through one or more gateways of the data processing system 120. The API call can be a call to conduct actions including SwitchtoDirectDeposit, SetUpMedicineCabinet, ChangeElectronicDelivery, AttachlnsuranceCarrier, AddMobilePhone, SignUpForAlerts, SignUpForElectronicTaxForms, and EnableLocationTracking.

The prescription action object 540 is configured to encapsulate at least one executable, module, link, and the like to execute or facilitate execution of at least one action based on the opportunity action object 540. The prescription action object 540 can include one or more instructions, restrictions, security policies, interfaces, and the like to modify the state of at least one medical records system associated with the prescription action object 540. As one example, a medical records system can include a pharmacy system, a health support system, a health insurance system, and the like. The prescription action object 540 include a cost encapsulator 542, the prescription linker 432, and the pharmacy linker 442.

The cost encapsulator 542 is configured to encapsulate at least one executable, module, link, and the like associated with at least one financial value, metric, or any combination thereof, for example, associated with the prescription object 430. As one example, the cost encapsulator 542 can include at least one of a price, pricing table, pricing modifier associated with at least one of the prescription object 430, the pharmacy object 440, and the drug object 460. The cost encapsulator 542 is configured to generate at least one modification to a cost value, metric, or any combination thereof, for example, associated with the prescription object 430, the pharmacy object 440, and the drug object 460. As one example, the cost encapsulator 542 is configured to generate a LongtermSavings value indicating a difference between cost for a prescription upon execution of an opportunity as compared to not executing the opportunity. It is to be understood that the cost encapsulator 542 is configured to achieve the technical solution of generating the LongtermSavings value based on multiple factors including multiple drugs that may be subject to cost modification based on multiple prescriptions from multiple health support systems and multiple pharmacy systems. The cost encapsulator 542 is configured to generate the LongtermSavings value with respect to a plurality of participants based on individualized records.

The account action object 550 is configured to encapsulate at least one executable, module, link, and the like to execute or facilitate execution of at least one action based on the opportunity action object 540. The account action object 550 can include one or more instructions, restrictions, security policies, interfaces, and the like to modify the state of at least one financial account system associated with the account action object 550. As one example, a financial account system can include a bank account, an HSA account, a flexible spending account (“FSA”) and the like. The account action object 550 can include an account identifier 552 and a cost encapsulator 554. The account action object 550 can include an account identifier 552 and a cost encapsulator 554. The account identifier 552 contains one or more characteristics associated with the financial account associated with the opportunity action object 530. The account identifier 552 can include one or more blocks, links, executables, or any combination thereof, for example, associated with a financial account system. As one example, the account identifier 552 is or encapsulates a financial account number, a financial account routing number, a financial account authorization code, a financial account authorization token, a financial account link, or any combination thereof, for example.

The cost encapsulator 554 is configured to encapsulate at least one executable, module, link, and the like associated with at least one financial value, metric, or any combination thereof, for example, associated with the account identifier 552. As one example, the cost encapsulator 542 can include at least one of a financial contribution amount, financial payment amount, financial credit amount, financial debit amount, or any combination thereof, for example, associated with the account identifier 552. The cost encapsulator 554 is configured to generate at least one modification to a cost value, metric, or any combination thereof, for example, associated with the account identifier 552. As one example, the cost encapsulator 542 is configured to generate a MaxOutContributions value instructing a financial account system to increase a payment to the financial account according to one or more threshold criteria contained in the account identifier 552. It is to be understood that the cost encapsulator 554 is configured to achieve the technical solution of generating the MaxOutContributions value based on secure direct communication with a financial account system.

FIG. 6 illustrates an example predictive scoring engine further to the example data processing system, in accordance with present implementations. In some implementations, an example scoring engine 400 can correspond to the scoring engine 330. As illustrated by way of example in FIG. 6, an example predictive scoring engine 600 can include the participant condition validator 322, the opportunity aggregation engine 324, and the service optimization engine 326.

The participant condition validator 322 is operable at least in accordance with the participant condition validator 322 of FIG. 3. The participant condition validator 322 can include one or more of a service analyzer 610, a participant analyzer 620, and a rating encapsulator 630.

The service analyzer 610 is operable to identify one or more services associated with a particular participant and a particular opportunity object associated with the participant. The service analyzer 610 is operable to identify one or more services compatible with an opportunity object. As one example, the service analyzer 610 can identify a participant financial account or a participant health insurance account associated with a participant and associated with an executable opportunity. The service analyzer 610 can include one or more of an insurance analyzer 612, an account analyzer 614, and a pharmacy analyzer 616. The service analyzer 610 is in real-time or substantially real-time in communication with one or more external service systems.

The insurance analyzer 612 is operable to identify at least one health insurance service associated with at least one of a participant and an opportunity object associated with the participant. The insurance analyzer 612 is operable to identify a type, category, class, group, or any combination thereof, for example, associated with an insurance service. As one example, the insurance analyzer 612 can identify a health insurance account as a health maintenance organization (HMO) account, a preferred provider organization (PPO) account, a health savings account (HSA), a flexible spending account (FSA), or any combination thereof, for example. In this example, the insurance analyzer 612 can generate an indication or any combination thereof, for example, whether a particular opportunity is associated with, compatible with, relevant to, or any combination thereof, for example, a particular insurance service.

The account analyzer 614 is operable to identify at least one financial service with at least one of a participant and an opportunity object associated with the participant. The account analyzer 614 is operable to identify a type, category, class, group, or any combination thereof, for example, associated with a financial service. As one example, the account analyzer 614 can identify a financial service as being associated with a checking account, a savings account, a credit account, a rewards account, a health savings account (HSA), a flexible spending account (FSA), or any combination thereof, for example. In this example, the account analyzer 614 can generate an indication or any combination thereof, for example, whether a particular opportunity is associated with, compatible with, relevant to, or any combination thereof, for example, a particular financial service.

The pharmacy analyzer 616 is operable to identify at least one pharmacy service with at least one of a participant and an opportunity object associated with the participant. The pharmacy analyzer 616 is operable to identify a type, category, class, group, or any combination thereof, for example, associated with a pharmacy service. As one example, the pharmacy analyzer 616 can identify at least one pharmacy service as being associated with at least one of a compatible insurance service and a compatible financial service as identified by the insurance analyzer 612 or the account analyzer 614. In this example, the pharmacy analyzer 616 can generate an indication or any combination thereof, for example, whether a particular opportunity is associated with, compatible with, relevant to, or any combination thereof, for example, a particular pharmacy service.

The participant analyzer 620 is operable to identify at least one participant characteristic associated with an opportunity object. The participant analyzer 620 is operable to identify one or more procedures, services, and the like, compatible with a particular participant or a particular opportunity associated with a participant. The participant analyzer 620 is operable to filter, block, isolate, select, rank, or any combination thereof, for example, one or more services identified by the service analyzer 610. As one example, the participant analyzer 620 can filter or block all pharmacy services not associated with a participant or participant object. The participant analyzer 620 can include one or more of a diagnostic analyzer 622, a prescription analyzer 624, a geospatial analyzer 626, and a biometric analyzer 628. The participant analyzer 620 is operable in real-time or substantially real-time in communication with one or more external service systems.

The diagnostic analyzer 622 is operable to identify at least one procedure associated with at least one health condition, state, metric, marker, biomarker, or any combination thereof, for example, of a participant. The diagnostic analyzer 622 is operable to match, correlate, generate, or any combination thereof, for example, a health procedure based on a health condition, state, metric, marker, biomarker, or any combination thereof, for example, of the participant obtained from a secure medical record at a health service provider, to generate at least one available procedure associated with a participant. As one example, the diagnostic analyzer 622 can identify a prescription, drug, surgery, or any combination thereof, for example, based on identifying a health condition including high blood pressure, prior arterial stent implantations, or any combination thereof, for example, associated with a participant. In this example, the diagnostic analyzer 622 can select an available procedure based on a correlation of one or more provider diagnostic metrics to the procedure. In some implementations, provider diagnostic metrics include individual medical records, health records, and the like. In some implementations, a correlation can include presence of a particular plurality of provider diagnostic metrics. In some implementations, a correlation can include a magnitude, degree, severity, or any combination thereof, for example, of one or more provider diagnostic metrics. The diagnostic analyzer 622 is operable to communicate in real-time or substantially real-time with one or more external secure health systems to identify available procedures.

The prescription analyzer 624 is operable to identify at least one prescription associated with at least one health condition, state, metric, marker, biomarker, or any combination thereof, for example, of a participant. The prescription analyzer 624 is operable to obtain, match, correlate, generate, or any combination thereof, for example, a prescription based on current, expired, or like prescriptions associated with or prescribed to a participant, to generate at least one available prescription associated with the participant. As one example, the prescription analyzer 624 can identify a prescription, drug, surgery, or any combination thereof, for example, from one or more secure external prescription heal records at one or more of an insurance service and a pharmacy service associated with the participant. The prescription analyzer 624 is operable to communicate in real-time or substantially real-time with one or more external secure health systems to identify prescriptions.

The geospatial analyzer 626 is operable to identify at least one physical location associated with a participant. The geospatial analyzer 626 is operable to obtain, match, correlate, generate, or any combination thereof, for example, a participant location based on current, previous, or like locations associated with the participant. As one example, the geospatial analyzer 626 can identify a geolocation including one or more of latitude, longitude, and elevation associated with the participant. The geospatial analyzer 626 is operable to generate one or more absolute or relative locations, distance, and the like, in real-time or substantially real-time with one or more external secure health systems.

The biometric analyzer 628 is operable to identify at least one procedure associated with at least one locally detected health condition, state, metric, marker, biomarker, or any combination thereof, for example, of a participant. The biometric analyzer 628 is operable to match, correlate, generate, or any combination thereof, for example, a health procedure based on a health condition, state, metric, marker, biomarker, or any combination thereof, for example, of the participant detected at a biometric user device, to generate at least one available procedure associated with a participant. As one example, the biometric analyzer 628 can identify a prescription, drug, surgery, or any combination thereof, for example, based on identifying a health condition including high blood pressure, irregular heartbeat, or any combination thereof, for example, associated with a participant. In this example, the biometric analyzer 628 can select an available procedure based on a correlation of one or more detected biometric diagnostic metrics to the procedure. In some implementations, detected biometric diagnostic metrics include steps, pulse, blood pressure, blood sugar, body temperature, and the like. In some implementations, a correlation can include presence of a particular plurality of biometric diagnostic metrics. In some implementations, a correlation can include a magnitude, degree, severity, or any combination thereof, for example, of one or more biometric diagnostic metrics. The biometric analyzer 628 is operable to communicate in real-time or substantially real-time with one or more external secure health systems to identify available procedures.

The rating encapsulator 630 is operable to generate, obtain, or any combination thereof, for example, one or more rating metrics associated with a participant object and a service. In some implementations, rating metrics include ratings obtained from a participant computing device 232 and indicating an absolute or relative ranking, rating, or any combination thereof, for example, of a service with respect to a participant. As one example, a participant can provide a quantitative “star” rating from between zero stars and five stars. As another example, a participant can provide a quantitative “favorites” or “preferences” ranking indicating services, service providers, or any combination thereof, for example, associated with higher probabilities of opportunity execution. As another example, a participant can provide a quantitative “disliked” or “not preferred” ranking indicating services, service providers, or any combination thereof, for example, associated with lower probabilities of opportunity execution. It is to be understood that the rating encapsulator 630 is operable to generate one or more rating metrics based on opportunity execution, selection, or any combination thereof, for example, associated with a particular participant object.

The opportunity aggregation engine 324 is operable to aggregate one or more metrics, characteristics, or any combination thereof, for example, associated with at least one selected service, with respect to a participant object and at least one aggregation criterion. In some implementations, a selected service can include a procedure, a prescription, a pharmacy, a physical location, or any combination thereof, for example, or a combination thereof. In some implementations, an aggregation criterion can include one or more of a time period, a frequency, a finite number of iterations, a financial cost, a financial savings, a time cost, a time savings, or any combination thereof, for example. As one example, the opportunity aggregation engine 324 can aggregate a total financial cost of a prescription over the course of a year. As another example, the opportunity aggregation engine 324 can aggregate a total time cost of travel to a pharmacy location from a home or office location. The opportunity aggregation engine 324 can include one or more of a service selector 640 and a service aggregator 650.

The service selector 640 is operable to select at least one service associated with a participant object. The service selector 640 is operable to select at least one service associated with a participant object in response to one or more identifications by at least one of the participant analyzer 620 and the diagnostic analyzer 622. The service selector 640 can include one or more of a procedure selector 642, a prescription selector 644, a pharmacy selector 646, and a geospatial selector 648.

The procedure selector 642 is operable to select at least one procedure associated with the participant object. The procedure selector 642 is operable to select at least one procedure identified by at least one of the diagnostic analyzer 622 and the biometric analyzer 628. As one example, the procedure selector 642 can select a pain reduction procedure associated with a joint sprain. The prescription selector 644 is operable to select at least one prescription associated with the participant object. The prescription selector 644 is operable to select at least one prescription associated with at least one selected procedure as selected by the procedure selector 642. As one example, the prescription selector 644 can select a pain reduction prescription associated with the pain reduction procedure for the joint sprain. It is to be understood that the opportunity aggregation engine 334 can optionally select a prescription by the prescription selector 644.

The pharmacy selector 646 is operable to select at least one pharmacy associated with the participant object. The pharmacy selector 646 is operable to select at least one pharmacy associated with at least one selected procedure as selected by the procedure selector 642. As one example, the pharmacy selector 646 can select a pharmacy that has in stock and available a pain reduction prescription associated with the pain reduction procedure for the joint sprain. In some implementations, an available prescription is available if at least one of the insurance analyzer 612 and the pharmacy analyzer 616 determines that the pharmacy accepts a health insurance service associated with the participant object. It is to be understood that the opportunity aggregation engine 334 can optionally select a pharmacy by the pharmacy selector 646.

The geospatial selector 648 is operable to select at least one physical location associated with the participant object. The geospatial selector 648 is operable to select at least one physical location associated with at least one selected procedure as selected by the procedure selector 642, or the participant object. As one example, the geospatial selector 648 can select a physical location associated with a home, office, current, preselected, hospital, primary care, emergency care or like physical location, and associated with the participant object. As one example, the geospatial selector 648 can select a geolocation, an address, a geofence, an elevation, or any combination thereof, for example, associated with a participant's home or preferred pharmacy. It is to be understood that the opportunity aggregation engine 334 can optionally select a pharmacy by the pharmacy selector 646.

The service aggregator 650 is operable to aggregate at least one metric, characteristic, or any combination thereof, for example, associated with at least one selected service. The service aggregator 650 can include one or more of a period selector 652, a cost aggregator 654, a geospatial aggregator 656, and an aggregation modeler 658.

The period selector 652 is operable to select at least one period associated with at least one of a procedure and a prescription. In some implementations, a period can include at least one of a time period, a frequency, a finite number of iterations, a financial cost, a financial savings, a time cost, a time savings, or any combination thereof, for example. As one example, a period can indicate a number of doses of a prescription. As another example, a period can indicate a term for consuming a prescription or conducting a procedure in hours, days, weeks, months, years, a lifetime, and the like.

The cost aggregator 654 is operable to aggregate at least one cost associated with at least one of a procedure and a prescription. In some implementations, a cost can include at least one of a time period, a frequency, a finite number of iterations, a financial cost, a financial savings, a time cost, a time savings, or any combination thereof, for example. As one example, the cost aggregator 654 can total a lifetime financial cost in dollars of purchasing a prescription associated with a participant procedure from a particular pharmacy service.

The geospatial aggregator 656 is operable to aggregate at least one aggregate physical parameter based on at least one physical location, where the physical location is associated with at least one of a procedure and a prescription. As one example, the geospatial aggregator 656 can total a lifetime distance in kilometers, miles, or any combination thereof, for example, of purchasing a prescription associated with a participant procedure from a particular pharmacy service. As another example, the geospatial aggregator 656 can total the lifetime distance based on a physical location associated with at least one of a home, office, hospital, primary care, emergency care, or any combination thereof, for example.

The aggregation modeler 658 is operable to generate at least one service model based on at least one aggregation over at least one period. The aggregation modeler 658 can include at least one machine learning (“ML”) engine. In some implementations, this ML engine obtains one or more inputs from one or more of the service selector 640 or any component thereof and the service aggregator and any component thereof. The aggregation modeler 658 obtains one or more inputs from one or more of its own outputs as feedback input. The aggregation modeler 658 generates one or more outputs in accordance with a learning model including one or more metrics, heuristics or any combination thereof, for example. The metrics, heuristics, or any combination thereof, for example, are obtained from at least one of the service selector 640 or any component thereof, the service aggregator 650, the period selector 652, the cost aggregator 654, and the geospatial aggregator 656. As one example, the aggregation modeler 658 can obtain a plurality of procedure types, a plurality of prescription types, a plurality of drugs, a plurality of pharmacy services, a plurality of insurance services, a plurality of diagnostics, a plurality of biometrics, a plurality of geospatial locations, and the like. The output of the aggregation modeler 658 can include at least one cost or aggregate physical location associated with a procedure, prescription, pharmacy, or any combination thereof, for example. As one example, the output of the aggregation modeler 658 can include a total lifetime cost of purchasing a prescription from a particular pharmacy. As another example, the output of the aggregation modeler 658 can include a total lifetime distance traveled to purchase a prescription from a particular pharmacy.

The opportunity aggregation engine 324 is operable at least in accordance with the participant condition validator 322 of FIG. 3. The opportunity aggregation engine 324 can include one or more of an opportunity linker 660, an optimizer metric extractor 670, and an opportunity optimizer 680.

The opportunity linker 660 contains a dynamic link to an opportunity object 510. The opportunity linker 660 is operable to generate, store, or any combination thereof, for example, a link, reference, or any combination thereof, for example, to the opportunity object 510 associated with the participant object 410. The participant profile linker 452 associates the participant profile object 450 with the participant object 410 in accordance with one or more of an import process of participant information from one or more third party administrator devices 240, or a selection, interaction, or any combination thereof, for example, received from a participant computing device 232 associated with the participant object 410. In some implementations, one or more of the participant condition validator 322, the opportunity aggregation engine 324, and the service optimization engine 326 obtain one or more opportunity objects by the opportunity linker 660.

The optimizer metric extractor 670 is operable to generate, obtain, or any combination thereof, for example, one or more metrics associated with at least one of the rating encapsulator 630, the prescription analyzer 624, the pharmacy analyzer 616, the geospatial analyzer 628, the service selector 640 or any component thereof, or any combination thereof. The metrics are inputs to an ML engine associated with the opportunity optimizer or any component thereof. The optimizer metric extractor 670 can include one or more of a rating extractor 672, a prescription extractor 674, a pharmacy extractor 676, and a geospatial extractor 678.

The rating extractor 672 is operable to extract one or more rating metrics associated with the rating encapsulator 630. The rating extractor 672 is operable to extract one or more types of ratings and rating values associated therewith. As one example, a type of rating can be a quality rating, a preference rating, or any combination thereof, for example, associated with a participant object. As another example, a rating value can be at least one of a discrete “star” rating, a discrete “out of 10” numerical rating, a tier level, or any combination thereof, for example.

The prescription extractor 674 is operable to extract one or more types of prescriptions and drugs associated therewith. As one example, a type of prescription can be a chronic condition prescription, an injury prescription, or any combination thereof, for example, associated with a participant object.

The pharmacy extractor 676 is operable to extract one or more pharmacy metrics associated with a selected pharmacy. The pharmacy extractor 676 is operable to extract one or more types of pharmacies and pharmacy values associated therewith. As one example, a type of pharmacy can be a walk up pharmacy, a drive-through pharmacy, an appointment pharmacy, a pharmacy with vaccine availability, or any combination thereof, for example. As another example, a pharmacy value can be at least one of a wait time, availability of drug, stock of drug, availability of pharmacist, or any combination thereof, for example.

The geospatial extractor 678 is operable to extract one or more geospatial metrics associated with at least one of a selected procedure and a selected pharmacy, with respect to a participant. The geospatial extractor 678 is operable to extract one or more geospatial types and geospatial values associated therewith. As one example, a geospatial type can be a geolocation, a geofence, an address, a distance, a type of travel, or any combination thereof, for example. As another example, a geospatial value can be at least one of a latitude, a longitude, an elevation, a postal address or any part thereof, a walking distance, a driving distance, or any combination thereof, for example.

The opportunity optimizer 680 is operable to generate one or more inferential models based on one or more service models. The opportunity optimizer 680 can include at least one ML engine. In some implementations, this ML engine obtains one or more inputs from the optimizer metric extractor 670 or any component thereof and the service aggregator 650 or any component thereof. The opportunity optimizer 680 obtains one or more inputs from one or more of its own outputs as feedback input. The opportunity optimizer 680 generates one or more outputs in accordance with a learning model including one or more metrics, heuristics or any combination thereof, for example. The metrics, heuristics, or any combination thereof, for example, are obtained from at least one of the optimizer metric extractor 670 or any component thereof, the service aggregator 650, the period selector 652, the cost aggregator 654, and the geospatial aggregator 656. As one example, the opportunity optimizer 680 can obtain a plurality of rating types, a plurality of prescription types, a plurality of drugs, a plurality of pharmacy services, a plurality of insurance services, a plurality of diagnostics, a plurality of biometrics, a plurality of geospatial locations, and the like, associated with a selected procedure, prescription, or any combination thereof, for example. The output of the opportunity optimizer 680 can include at least one cost or aggregate physical location associated with a model, for example. As one example, the output of the opportunity optimizer 680 can include a model of total lifetime cost, over time, of purchasing a prescription from a particular pharmacy. As another example, the output of the opportunity optimizer 680 can include a total lifetime distance traveled, over time, to purchase a prescription from a particular pharmacy. The opportunity optimizer 680 can include a plurality of concurrently presented service models and a comparative analysis of one or more thereof. The opportunity optimizer 680 can include one or more of a comparison modeler 682, a cost optimizer 684, and a geospatial optimizer 686.

The comparison modeler 682 is operable to generate one or more comparative models associated with a plurality of service models. The comparison modeler 682 is operable to generate a total comparative output indicating a total difference between a plurality of service models based on one or more metrics. The comparison modeler is operable to obtain one or more metrics from the optimizer metric extractor 670. As one example, the comparison modeler 682 is operable to generate a lifetime cost difference between purchasing a prescription from a pharmacy associated with a participant object and purchasing a prescription from a pharmacy not associated with the participant object and associated with an opportunity object. The comparison modeler 682 is operable to at least one output associated with various values of a target metric. The comparison modeler 682 is operable to obtain one or more service models from at least one of the service aggregator 650 and the aggregation modeler 658. The comparison modeler 682 is operatively coupled to one or more third party administrator devices 240 in real-time or substantially real-time, and is operable to modify, update, generate, obtain, or any combination thereof, for example, one or more comparative models in real-time or substantially real-time based on real-time or substantially real-time communication with the third party administrator devices 240.

The cost optimizer 684 is operable to generate a comparative model based on financial cost. The cost optimizer 684 is operable to generate a comparative model based on input from one or more of the account analyzer 614, the service selector 640 or any component thereof, and the optimizer metric extractor 670 or any component thereof. As one example, a target metric may be purchase cost of a prescription saved, and various values may be dates at monthly intervals in the future. As another example, a target metric may be fuel cost saved from driving to a closer pharmacy to purchase a prescription, and various values may be dates at monthly intervals in the future.

The geospatial optimizer 686 is operable to generate a comparative model based on one or more physical locations, distances, or any combination thereof, for example. The geospatial optimizer 686 is operable to generate a comparative model based on input from one or more of the geospatial analyzer 626, the service selector 640 or any component thereof, and the optimizer metric extractor 670 or any component thereof. As one example, a target metric may be time saved, and various values may be dates at monthly intervals in the future.

FIG. 7 illustrates an example electronic device associated with an example data processing system and operating in an example temporal comparison modeling state, in accordance with present implementations. As illustrated by way of example in FIG. 7, an example electronic device 700 can include a service metrics summary region 702, a score summary region 704, an optimized metrics summary region 706, and a metric model presentation region 710. The example electronic device 600 can include a processor, memory device, and display device to generate a graphical user interface, and a communication interface to communication with the data processing system as discussed above with respect to FIGS. 1A-D and 2. The example electronic device 600 is operable to generate a graphical user interface (“GUI”) representing, displaying, and the like, one or more of objects of FIGS. 4 and 5. In some implementations, The example electronic device 600 can include a GUI associated with a mobile operating system. In some implementations, The example electronic device 600 is further operable to receive selection, input, and the like from a touch-based input system disposed thereon, therewith, or any combination thereof, for example. The touch-based input system is a capacitive or a resistive touch interface. In some implementations, that the mobile interface can include a presentation device including but not limited to an LCD, LED, OLED or any combination thereof, for example.

The service metrics summary region 702 is operable to present one of more summary metrics associated with a service, selected service, or any combination thereof, for example. The service metrics summary region 702 can include a summary of one or more metrics associated with a service currently associated with a participant object. The service metrics summary region 702 is capable of being updated in real-time or substantially real-time in communication with one or more third party administrator devices 240. The score summary region 704 is operable to present at least one metric associated with one or more opportunity objects available for selection at the example electronic device 700. The score summary region 704 presents at least one execution value associated with, generated based on, or any combination thereof, for example, one or more executed opportunity objects. The score summary region 704 presents an aggregation of a plurality of execution values each associated with a particular point value. The score summary region 704 is capable of being updated in real-time or substantially real-time in communication with one or more third party administrator devices 240.

The optimized metrics summary region 706 is operable to present one of more summary metrics associated with a service, selected service, or any combination thereof, for example. The optimized metrics summary region 706 can include a summary of one or more metrics associated with a service not currently associated with a participant object. The optimized metrics summary region 706 can include a summary of one or more metrics associated with a service associated with an opportunity object and executable in response to a selection thereof at the electronic device 700. The optimized metrics summary region 706 is capable of being updated in real-time or substantially real-time in communication with one or more third party administrator devices 240.

The metric model presentation region 710 is operable to present at least one service model, comparative model, or any combination thereof, for example. The metric model presentation region 710 can include one or more of a first metric axis 712, a second metric axis 714, and a metric performance characteristic 716. The metric model presentation region 710 is capable of being updated in real-time or substantially real-time in communication with one or more third party administrator devices 240.

The first metric axis 712 can include a first metric associated with a participant object. The first metric axis 712 is associated with a time period for consumption of a service associated with a participant object. As one example, the first metric axis 712 can be a time axis measured in hours, days, weeks, months, years, or any combination thereof, for example, and can be variable in scale in response to a time period associated with the period selector.

The second metric axis 714 can include a second metric associated with at least one of an opportunity object, a selected service, and a selected procedure. The second metric axis 714 is associated with a time cost, financial cost, or any combination thereof, for example, for consumption of a service associated with a participant object. As one example, the second metric axis 714 can be a time axis measured in hours, days, weeks, months, years, or any combination thereof, for example, and can be variable in scale in response to a time period associated with the period selector. As another example, the time axis can indicate an amount of time saved by executing a particular opportunity object in accordance with a comparative model based on the opportunity object and a current service selection associated with a participant object.

The metric performance characteristic 716 can include at least one representation of a service model or a comparative model. The metric performance characteristic 716 represents a single service model. The metric performance characteristic 716 represents a single comparative model. The metric performance characteristic 716 represents an aggregation of multiple service models. The metric performance characteristic 716 represents an aggregation of multiple comparative models.

FIG. 8 illustrates an example electronic device associated with an example data processing system and operating in an example geospatial comparison modeling state, in accordance with present implementations. As illustrated by way of example in FIG. 8, an example electronic device 800 can include the score summary region 704, a service summary region 802, an optimized service summary region 804, and a metric model presentation region 810. The an example electronic device 800 can correspond to the an example electronic device 700 in at least one of structure, operation, composition, dimensions, and the like.

The service summary region 802 is operable to present one of more summary factors associated with a service, selected service, or any combination thereof, for example. The service summary region 802 can include a summary of one or more factors associated with a service currently associated with a participant object. In some implementations, factors include service name, procedure name, address, cost, price or any combination thereof, for example. The service summary region 802 is capable of being updated in real-time or substantially real-time in communication with one or more third party administrator devices 240.

The optimized service summary region 804 is operable to present at least one summary factor associated with a service, selected service, or any combination thereof, for example. The optimized service summary region 804 can include a summary of one or more metrics associated with a service not currently associated with a participant object. The optimized service summary region 804 can include a summary of one or more factors associated with a service associated with an opportunity object and executable in response to a selection thereof at the electronic device 700. The optimized service summary region 804 is capable of being updated in real-time or substantially real-time in communication with one or more third party administrator devices 240.

The metric model presentation region 810 is operable to present at least one geospatial representation associated with at least one of a current participant service associated with a participant object, a service associated with an opportunity object, and a metric object indicating a relative or absolute property of one or more services. The metric model presentation region 810 can include a map and at least one overlay associated with a service or a participant. The metric model presentation region 810 can include one or more of a participant geolocation 820, a participant geofence 822, a service identifier 830, and an optimized service identifier 840. The metric model presentation region 810 is capable of being updated in real-time or substantially real-time in communication with one or more third party administrator devices 240.

The participant geolocation 820 can include a current, predicted, expected, past, detected, obtained, or like geolocation associated with a participant object or the electronic device 800. The participant geofence 822 can include at least one region, boundary, area, locale, or any combination thereof, for example, associated with a participant metric, rating, or any combination thereof, for example. As one example, the participant geofence 822 can be a maximum distance or time the participant is willing or able to travel to obtain one or more services. It is to be understood that the participant geofence 822 can take any shape, including but not limited to a round, polygonal, contoured, or like closed shape, or any open line, arc or collection thereof.

The service identifier 830 can include an indication of a current or like service associated with the participant account. The service identifier 830 is presented as a map pin at a particular physical location therewith. As one example, the service identifier 830 can be a pharmacy associated with a participant's account, or rated as a preferred provider by the participant or the participant's insurance provider. The optimized service identifier 840 can include an indication of a service associated with an opportunity object. The optimized service identifier 840 is presented as a map pin at a particular physical location therewith. The optimized service identifier 840 is a pharmacy having a particular metric satisfying a metric presentation condition, or presented in accordance with one or more of a service model and a comparative model. As one example, the optimized service identifier 840 can be a pharmacy service having a time, distance, financial, or like cost metric lower than a corresponding metric associated with the service identifier 830. It is to be understood that the optimized service identifier 840 can be outside of or presented independently of the participant geofence 822. It is to be further understood that the optimized service identifier 840 can be presented on an aggregation of a plurality of metrics in accordance with at least one of a service model and a comparative model. It is to be further understood that factors the optimized service identifier 840 can be presented based on cost savings or a plurality of metrics resulting in presentation outside of the participant geofence 822.

FIG. 9 illustrates an example method of generating a real-time trigger-based digital feed, in accordance with present implementations. In some implementations, at least one of the example systems 300, 400, and 500 and the example device 600 performs method 900 according to present implementations. The method 900 begins at step 910.

At step 910, the example system generates historical condition-service data. It is to be understood that historical condition-service data can be any input to the service optimization engine 326, and can include one or more metrics of the optimizer metric extractor 670 or any component thereof. Step 910 can include at least one of steps 912 and 914. At step 912, the example system generates historical condition-service data based on a first participant service system. At step 914, the example system generates historical condition-service data based on a second participant service system. The method 900 then continues to step 920.

At step 920, the example system obtains one or more condition factors associated with at least one participant object. Step 920 can include at least one of steps 922 and 924. At step 922, the example system obtains a condition metric associated with the participant object. At step 924, the example system obtains a condition period associated with the participant object. The method 900 then continues to step 930.

At step 930, the example system generates a first participant metric associated with the participant object. Step 930 can include at least one of steps 932 and 934. At step 932, the example system generates a first participant metric associated with the participant object by the model trained on the historical condition-service data. At step 934, the example system generates a first participant metric associated with the participant object based on a participant service associated with the condition metric and the participant object. The method 900 then continues to step 940.

At step 940, the example system aggregates the first participation metric and the condition period into a first aggregated participation metric. The method 900 then continues to step 1002.

FIG. 10 illustrates an example method of generating a real-time trigger-based digital feed further to the method of FIG. 9, in accordance with present implementations. In some implementations, at least one of the example systems 300, 400, and 500 and the example device 600 performs method 1000 according to present implementations. The method 1000 begins at step 1002. The method 1000 then continues to step 1010.

At step 1010, the example system receives a selection of an opportunity object associated with a condition metric. Step 1010 can include at least one of steps 1012 and 1014. At step 1012, the example system receives a selection of an opportunity object associated with a condition metric via a computing device associated with a participant object. At step 1014, the example system a selection of an opportunity object associated with a condition metric and for an opportunity event, via a third party application. The method 1000 then continues to step 1020.

At step 1020, the example system generates a second participation metric associated with the participant object. Step 1020 can include step 1022. At step 1022, the example system generates a second participation metric associated with the participant object based on a condition metric and an opportunity object. The method 1000 then continues to step 1030.

At step 1030, the example system aggregates the second participation metric and the condition period into a second aggregated participation metric. The method 1000 then continues to step 1040.

At step 1040, the example system determines a reduction in resource utilization. Step 1040 can include step 1042. At step 1042, the example system determines a reduction in resource utilization where the second aggregated participation metric is less than the first aggregated participation metric. It is to be understood that the example system can determine a reduction in resource utilization based on any arithmetic or logical relationship, for example, between the first aggregated participation metric and the second aggregated participation metric. The method 1000 then continues to step 1050.

At step 1050, the example system provides an alert indicating a reduction in resource utilization. Step 1050 can include step 1052. At step 1052, the example system ides an alert indicating a reduction in resource utilization to a computing device associated with one or more of a participant object and a received selection. The method 1000 ends at step 1050.

It should be understood that the systems described above can provide multiple ones of any or each of those components and these components can be provided on either a standalone machine or, in some implementations, on multiple machines in a distributed system. The systems and methods described above can be implemented as a method, apparatus or article of manufacture using programming or engineering techniques to produce software, firmware, hardware, or any combination thereof. In addition, the systems and methods described above can be provided as one or more computer-readable programs embodied on or in one or more articles of manufacture. The term “article of manufacture” as used herein is intended to encompass code or logic accessible from and embedded in one or more computer-readable devices, firmware, programmable logic, memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs, SRAMs, etc.), hardware (e.g., integrated circuit chip, Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), etc.), electronic devices, a computer readable non-volatile storage unit (e.g., CD-ROM, floppy disk, hard disk drive, etc.). The article of manufacture can be accessible from a file server providing access to the computer-readable programs via a network transmission line, wireless transmission media, signals propagating through space, radio waves, infrared signals, etc. The article of manufacture can be a flash memory card or a magnetic tape. The article of manufacture includes hardware logic as well as software or programmable code embedded in a computer readable medium that is executed by a processor. In general, the computer-readable programs can be implemented in any programming language, such as LISP, PERL, C, C++, C#, PROLOG, or in any byte code language such as JAVA. The software programs can be stored on or in one or more articles of manufacture as object code.

The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are illustrative, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable,” to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components

With respect to the use of plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.

It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.).

Although the figures and description may illustrate a specific order of method steps, the order of such steps may differ from what is depicted and described, unless specified differently above. Also, two or more steps may be performed concurrently or with partial concurrence, unless specified differently above. Such variation may depend, for example, on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations of the described methods could be accomplished with standard programming techniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps, and decision steps.

It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation, no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations).

Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general, such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”

Further, unless otherwise noted, the use of the words “approximate,” “about,” “around,” “substantially,” etc., mean plus or minus ten percent.

The foregoing description of illustrative implementations has been presented for purposes of illustration and of description. It is not intended to be exhaustive or limiting with respect to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of the disclosed implementations. It is intended that the scope of the invention be defined by the claims appended hereto and their equivalents. 

1.-20. (canceled)
 21. A system, comprising: a data processing system comprising memory and one or more processors to: predict, via input of a condition of a participant into a model trained with machine learning and historical data for conditions and services, a first metric corresponding to consumption of a service for the condition by the participant over a time period; identify an execution of an action between a computing device linked with the participant and an electronic account; update, via input of the execution of the action into the model, the prediction of the first metric to a second metric of the consumption of the service by the participant, wherein the first metric of the consumption is predicted for the participant over the time period in the absence of the execution of the action; determine, based on a comparison of the first metric with the second metric, that the second metric corresponds to a reduction in the consumption of the service over the time period; and provide, via a network to the computing device responsive to the determination of the reduction in the consumption, an alert that indicates the reduction in the consumption due to the execution of the action.
 22. The system of claim 21, comprising: the data processing system to detect the execution of the action between the computing device and the electronic account established by a third-party administrator device for the participant.
 23. The system of claim 21, comprising: the data processing system to obtain, via the network from a third-party administrator device associated with the electronic account, the condition and the time period for the participant.
 24. The system of claim 21, comprising: the data processing system to obtain, via the network from a third-party administrator device associated with the electronic account, a profile for the participant, the profile comprising the condition and the time period.
 25. The system of claim 21, comprising the data processing system to: aggregate a pattern of the consumption of the service by the participant over the time period to predict the first metric; and update the pattern of the aggregation of the consumption of the service by the participant over the time period to predict the second metric.
 26. The system of claim 21, comprising: the data processing system to receive a selection of an opportunity object by the computing device, the opportunity object comprising the action.
 27. The system of claim 21, comprising: the data processing system to detect a selection of an opportunity object corresponding to an opportunity event provided by a third-party application that interfaces with the data processing system, the opportunity object comprising the action provided by a third-party administrator device that established the electronic account, wherein the action is established for the participant.
 28. The system of claim 21, wherein the condition comprises a health state, the first metric comprises a first monetary amount associated with the health state, and the second metric comprises a second monetary amount associated with the health state.
 29. The system of claim 21, wherein the reduction in the consumption of the service comprises a reduction by in only the service by the participant by a difference between a numeric value of the first metric and the second metric.
 30. The system of claim 21, comprising the data processing system to: determine the first metric based at least in part on a first rating of the service associated with the condition and a location of the participant; and determine the second metric based at least in part on a second rating of the service, the action, and a distance heuristic associated with the location.
 31. A method, comprising: predicting, by a data processing system comprising memory and one or more processors, via input of a condition of a participant into a model trained with machine learning and historical data for conditions and services, a first metric corresponding to consumption of a service for the condition by the participant over a time period; identifying, by the data processing system, an execution of an action between a computing device linked with the participant and an electronic account; updating, by the data processing system via input of the execution of the action into the model, the prediction of the first metric to a second metric of the consumption of the service by the participant, wherein the first metric of the consumption is predicted for the participant over the time period in the absence of the execution of the action; determining, by the data processing system based on a comparison of the first metric with the second metric, that the second metric corresponds to a reduction in the consumption of the service over the time period; and providing, by the data processing system, via a network to the computing device responsive to the determination of the reduction in the consumption, an alert that indicates the reduction in the consumption due to the execution of the action.
 32. The method of claim 31, comprising: detecting, by the data processing system, the execution of the action between the computing device and the electronic account established by a third-party administrator device for the participant.
 33. The method of claim 31, comprising: obtaining, by the data processing system, via the network from a third-party administrator device associated with the electronic account, the condition and the time period for the participant.
 34. The method of claim 31, comprising: obtaining, by the data processing system, via the network from a third-party administrator device associated with the electronic account, a profile for the participant, the profile comprising the condition and the time period.
 35. The method of claim 31, comprising: aggregating, by the data processing system, a pattern of the consumption of the service by the participant over the time period to predict the first metric; and updating, by the data processing system, the pattern of the aggregation of the consumption of the service by the participant over the time period to predict the second metric.
 36. The method of claim 31, comprising: receiving, by the data processing system, a selection of an opportunity object by the computing device, the opportunity object comprising the action.
 37. The method of claim 31, comprising: detecting, by the data processing system, a selection of an opportunity object corresponding to an opportunity event provided by a third-party application that interfaces with the data processing system, the opportunity object comprising the action provided by a third-party administrator device that established the electronic account, wherein the action is established for the participant.
 38. The method of claim 31, wherein the condition comprises a health state, the first metric comprises a first monetary amount associated with the health state, and the second metric comprises a second monetary amount associated with the health state.
 39. The method of claim 31, wherein the reduction in the consumption of the service comprises a reduction by in only the service by the participant by a difference between a numeric value of the first metric and the second metric.
 40. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: predict, via input of a condition of a participant into a model trained with machine learning and historical data for conditions and services, a first metric corresponding to consumption of a service for the condition by the participant over a time period; identify an execution of an action between a computing device linked with the participant and an electronic account; update, via input of the execution of the action into the model, the prediction of the first metric to a second metric of the consumption of the service by the participant, wherein the first metric of the consumption is predicted for the participant over the time period in the absence of the execution of the action; determine, based on a comparison of the first metric with the second metric, that the second metric corresponds to a reduction in the consumption of the service over the time period; and provide, via a network to the computing device responsive to the determination of the reduction in the consumption, an alert that indicates the reduction in the consumption due to the execution of the action. 